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@InCollection{Dennis2004,
author = {Dennis, Brian},
title = {Statistics and the Scientific Method in Ecology},
chapter = {11},
creationdate = {2014-10-31T00:00:00},
crossref = {Taper2004},
file = {:Dennis2004.pdf:PDF},
modificationdate = {2022-07-20T09:45:25},
owner = {kingaa},
}
@Book{Albert2007,
author = {Albert, Jim},
publisher = {Springer},
title = {Bayesian Computation with R},
year = {2007},
address = {New York},
note = {ISBN 978-0-387-71384-7},
abstract = {Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:22},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springer.com/978-0-387-71384-7},
}
@Article{Anderson2002,
author = {Anderson, D. R. and Burnham, K. P.},
journal = {Journal of Wildlife Management},
title = {Avoiding Pitfalls When Using Information-Theoretic Methods},
year = {2002},
pages = {912--918},
volume = {66},
creationdate = {2008-01-06T00:00:00},
modificationdate = {2022-07-20T09:45:22},
owner = {kingaa},
}
@Article{Andrieu2010,
author = {Andrieu, Christophe and Doucet, Arnaud and Holenstein, Roman},
journal = {Journal of the Royal Statistical Society, Series B},
title = {{P}article {M}arkov chain {M}onte {C}arlo methods},
year = {2010},
number = {3},
pages = {269--342},
volume = {72},
abstract = {Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as the two main tools to sample from high dimensional probability distributions. Although asymptotic convergence of Markov chain Monte Carlo algorithms is ensured under weak assumptions, the performance of these algorithms is unreliable when the proposal distributions that are used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. We show here how it is possible to build efficient high dimensional proposal distributions by using sequential Monte Carlo methods. This allows us not only to improve over standard Markov chain Monte Carlo schemes but also to make Bayesian inference feasible for a large class of statistical models where this was not previously so. We demonstrate these algorithms on a non-linear state space model and a Levy-driven stochastic volatility model.},
creationdate = {2010-06-30T00:00:00},
doi = {10.1111/j.1467-9868.2009.00736.x},
file = {Andrieu2010.pdf:Andrieu2010.pdf:PDF},
owner = {kingaa},
}
@Article{Anonymous1978,
author = {Anonymous},
journal = {British Medical Journal},
title = {{I}nfluenza in a boarding school},
year = {1978},
pages = {587},
volume = {1},
creationdate = {2008-07-11T00:00:00},
file = {Anonymous1978.pdf:Anonymous1978.pdf:PDF},
owner = {kingaa},
}
@Article{Arulampalam2002,
author = {Arulampalam, M. S. and Maskell, S. and Gordon, N. and Clapp, T.},
journal = {IEEE Transactions on Signal Processing},
title = {A tutorial on particle filters for online nonlinear, non-{G}aussian {B}ayesian tracking},
year = {2002},
pages = {174--188},
volume = {50},
creationdate = {2011-07-28T00:00:00},
doi = {10.1109/78.978374},
file = {Arulampalam2002.pdf:Arulampalam2002.pdf:PDF},
owner = {kingaa},
}
@Book{Becker1988,
author = {Becker, Richard A. and Chambers, John M. and Wilks, Allan R.},
publisher = {Chapman \& Hall},
title = {The New {S} Language},
year = {1988},
address = {London},
abstract = {This book is often called the ``\emph{Blue Book}'', and introduced what is now known as S version 2.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:22},
owner = {kingaa},
}
@Book{Behr2005,
author = {Behr, Andreas},
publisher = {Vahlen},
title = {Einf\"uhrung in die Statistik mit {R}},
year = {2005},
address = {M\"unchen},
isbn = {3-8006-3219-5},
note = {ISBN 3-8006-3219-5, in German},
series = {WiSo Kurzlehrb\"ucher},
creationdate = {2007-12-31T00:00:00},
language = {de},
modificationdate = {2022-07-20T09:45:23},
owner = {kingaa},
}
@Book{Boland2007,
author = {Boland, Philip J.},
publisher = {Chapman \& Hall/CRC},
title = {Statistical and Probabilistic Methods in Actuarial Science},
year = {2007},
address = {Boca Raton, FL},
note = {ISBN 1-584-88695-1},
abstract = {This book covers many of the diverse methods in applied probability and statistics for students aspiring to careers in insurance, actuarial science, and finance. It presents an accessible, sound foundation in both the theory and applications of actuarial science. It encourages students to use the statistical software package R to check examples and solve problems.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {crcpress.txt},
owner = {kingaa},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C6951},
}
@Article{Bolker2009a,
author = {Bolker, Ben},
journal = {Ecological Applications},
title = {{L}earning hierarchical models: advice for the rest of us},
year = {2009},
month = apr,
number = {3},
pages = {588--592},
volume = {19},
creationdate = {2009-12-10T00:00:00},
doi = {10.1890/08-0639.1},
file = {Bolker2009a.pdf:Bolker2009a.pdf:PDF},
owner = {kingaa},
}
@Manual{bbmle,
title = {bbmle: Tools for general maximum likelihood estimation},
author = {Bolker, Ben and {R Development Core Team}},
note = {R package version 1.0.17},
year = {2014},
modificationdate = {2022-07-20T09:45:23},
url = {http://CRAN.R-project.org/package=bbmle},
}
@Article{Bolker2009c,
author = {Bolker, Benjamin M. and Brooks, Mollie E. and Clark, Connie J. and Geange, Shane W. and Poulsen, John R. and Stevens, M. Henry H. and White, Jada-Simone S.},
journal = {Trends in Ecology and Evolution},
title = {Generalized linear mixed models: a practical guide for ecology and evolution},
year = {2009},
number = {3},
pages = {127--135},
volume = {24},
creationdate = {2014-11-25T00:00:00},
doi = {10.1016/j.tree.2008.10.008},
file = {:Bolker2009c.pdf:PDF},
groups = {[kingaa:]},
modificationdate = {2023-09-01T09:24:46},
owner = {kingaa},
publisher = {Elsevier},
}
@Book{Braun2007,
author = {Braun, W. John and Murdoch, Duncan J.},
publisher = {Cambridge University Press},
title = {A First Course in Statistical Programming with R},
year = {2007},
address = {Cambridge},
note = {ISBN 978-0521872652},
abstract = {This book introduces students to statistical programming, using R as a basis. Unlike other introductory books on the R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
owner = {kingaa},
pages = {362},
publisherurl = {http://www.cambridge.org/us/catalogue/catalogue.asp?isbn=9780521872652},
url = {http://www.stats.uwo.ca/faculty/braun/statprog/},
}
@Book{Burns2012,
author = {Burns, Patrick},
publisher = {http://www.burns-stat.com/},
title = {The {R} {I}nferno},
year = {2012},
edition = {2\textsuperscript{nd}},
creationdate = {2014-09-17T00:00:00},
file = {Burns2012.pdf:Burns2012.pdf:PDF},
modificationdate = {2022-07-20T09:45:25},
owner = {kingaa},
url = {http://www.burns-stat.com/pages/Tutor/R_inferno.pdf},
}
@Article{Caswell1988,
author = {Caswell, Hal},
journal = {Ecological Modelling},
title = {Theory and models in ecology: A different perspective},
year = {1988},
month = oct,
number = {1-2},
pages = {33--44},
volume = {43},
abstract = {Many widespread criticisms of ecological theory, especially theory relying on mathematical models, are based on misunderstandings the role of models in theory and of theory in the larger discipline of ecology. The naivete of some of these misunderstandings suggests that they are too seldom examined. In this paper, I address what I consider to be the most ill-conceived criticisms of ecological theory. I propose that the parallels between theoretical and empirical research are more profound than most people realize. Appreciation of these parallels will go a long way towards bridging the gap between empiricists and theoreticians in ecology},
creationdate = {2008-01-06T00:00:00},
doi = {10.1016/0304-3800(88)90071-3},
owner = {kingaa},
}
@Book{Chambers2007,
author = {Chambers, John M.},
publisher = {Springer},
title = {Software for Data Analysis: Programming with R},
year = {2007},
address = {New York},
note = {ISBN 978-0-387-75935-7},
abstract = {John Chambers has been the principal designer of the S language since its beginning, and in 1999 received the ACM System Software award for S, the only statistical software to receive this award. He is author or coauthor of the landmark books on S. Now he turns to R, the enormously successful open-source system based on the S language. R's international support and the thousands of packages and other contributions have made it the standard for statistical computing in research and teaching. This book guides the reader through programming with R, from interactive use through all stages from simple functions to the design of R packages. It includes key modern enhancements such as classes and methods, namespaces and interfaces to spreadsheets and data bases.},
creationdate = {2007-12-31T00:00:00},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http:///www.springer.com/978-0-387-75935-7},
}
@Book{Chambers1998,
author = {Chambers, John M.},
publisher = {Springer},
title = {Programming with Data},
year = {1998},
address = {New York},
note = {ISBN 0-387-98503-4},
abstract = {This ``\emph{Green Book}'' describes version 4 of S, a major revision of S designed by John Chambers to improve its usefulness at every stage of the programming process.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40109-22-2008951-0,00.html},
url = {http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/},
}
@Book{Chambers1992,
author = {Chambers, John M. and Hastie, Trevor J.},
publisher = {Chapman \& Hall},
title = {Statistical Models in {S}},
year = {1992},
address = {London},
abstract = {This is also called the ``\emph{White Book}'', and introduced S version 3, which added structures to facilitate statistical modeling in S.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {crcpress.txt},
owner = {kingaa},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C3040&parent_id=&pc=},
}
@Article{Chen2004,
author = {Chen, Y.},
journal = {Nonlinear Analysis: Real World Applications},
title = {Multiple periodic solutions of delayed predator-prey systems with type {IV} functional responses},
year = {2004},
pages = {45--53},
volume = {5},
creationdate = {2008-01-28T00:00:00},
modificationdate = {2022-07-20T09:45:23},
owner = {kingaa},
}
@Article{Chib2005,
author = {Chib, Siddhartha and Jeliazkov, Ivan},
journal = {Statistica Neerlandica},
title = {Accept--reject Metropolis--Hastings sampling and marginal likelihood estimation},
year = {2005},
number = {1},
pages = {30--44},
volume = {59},
creationdate = {2014-11-18T00:00:00},
doi = {10.1111/j.1467-9574.2005.00277.x},
file = {:Chib2005.pdf:PDF},
groups = {[kingaa:]},
owner = {kingaa},
publisher = {Wiley Online Library},
}
@Article{Chib2001,
author = {Chib, Siddhartha and Jeliazkov, Ivan},
journal = {Journal of the American Statistical Association},
title = {Marginal likelihood from the Metropolis--Hastings output},
year = {2001},
number = {453},
pages = {270--281},
volume = {96},
creationdate = {2014-11-18T00:00:00},
doi = {10.1198/016214501750332848},
file = {:Chib2001.pdf:PDF},
groups = {[kingaa:]},
owner = {kingaa},
publisher = {Taylor \& Francis},
}
@Article{Christensen2014,
author = {Christensen, Bj{\"o}rn and Christensen, S{\"o}ren},
journal = {Proceedings of the National Academy of Sciences},
title = {Are female hurricanes really deadlier than male hurricanes?},
year = {2014},
number = {34},
pages = {E3497--E3498},
volume = {111},
creationdate = {2014-11-17T00:00:00},
groups = {[kingaa:]},
owner = {kingaa},
publisher = {National Acad Sciences},
}
@Book{Clark2007,
author = {Clark, James},
publisher = {Princeton University Press},
title = {Models for ecological data : an introduction},
year = {2007},
address = {Princeton},
isbn = {9780691121789},
creationdate = {2014-10-31T00:00:00},
owner = {kingaa},
}
@Article{Clark2005,
author = {Clark, James S.},
journal = {Ecology Letters},
title = {Why environmental scientists are becoming {B}ayesians},
year = {2005},
number = {1},
pages = {2--14},
volume = {8},
creationdate = {2014-10-30T00:00:00},
doi = {10.1111/j.1461-0248.2004.00702.x},
file = {:Clark2005.pdf:PDF},
groups = {[kingaa:]},
modificationdate = {2022-07-20T09:45:25},
owner = {kingaa},
publisher = {Wiley Online Library},
}
@Article{Clark1999,
author = {Clark, J. S. and Silman, M. and Kern, R. and Macklin, E. and HilleRisLambers, J.},
journal = {Ecology},
title = {Seed dispersal near and far: patterns across temperate and tropical forests},
year = {1999},
pages = {1475--1494},
volume = {80},
creationdate = {2008-02-04T00:00:00},
modificationdate = {2022-07-20T09:45:23},
owner = {kingaa},
}
@Book{Cook2007,
author = {Cook, Dianne and Swayne, Deborah F.},
publisher = {Springer},
title = {Interactive and Dynamic Graphics for Data Analysis},
year = {2007},
address = {New York},
note = {ISBN 978-0-387-71761-6},
abstract = {This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapters include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The role of graphical methods is shown at each step of the analysis, not only in the early exploratory phase, but in the later stages, too, when comparing and evaluating models. All examples are based on freely available software: GGobi for interactive graphics and R for static graphics, modeling, and programming. The printed book is augmented by a wealth of material on the web, encouraging readers follow the examples themselves. The web site has all the data and code necessary to reproduce the analyses in the book, along with movies demonstrating the examples.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springer.com/978-0-387-71761-6},
}
@Book{Crawley2005,
author = {Crawley, Michael J.},
publisher = {Wiley},
title = {Statistics: An Introduction using R},
year = {2005},
note = {ISBN 0-470-02297-3},
abstract = {The book is primarily aimed at undergraduate students in medicine, engineering, economics and biology --- but will also appeal to postgraduates who have not previously covered this area, or wish to switch to using R.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
owner = {kingaa},
publisherurl = {http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470022973.html},
url = {http://www.bio.ic.ac.uk/research/crawley/statistics/},
}
@InCollection{Crome1997,
author = {Crome, Francis H. J.},
booktitle = {Tropical forest remnants: ecology, management, and conservation of fragmented communities},
publisher = {University of Chicago Press},
title = {Researching tropical forest fragmentation: Shall we keep on doing what we're doing?},
year = {1997},
address = {Chicago, IL},
chapter = {31},
editor = {Laurance, W. F. and Bierregard, R. O.},
pages = {485--501},
modificationdate = {2022-07-20T09:45:23},
}
@Book{Dalgaard2002,
author = {Dalgaard, Peter},
publisher = {Springer},
title = {Introductory Statistics with {R}},
year = {2002},
note = {ISBN 0-387-95475-9},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {springer.txt},
owner = {kingaa},
pages = {288},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-10130-22-2287329-0,00.html?changeHeader=true},
url = {http://www.biostat.ku.dk/~pd/ISwR.html},
}
@Article{Davidson1948,
author = {Davidson, J. and Andrewartha, H. G.},
journal = {Journal of Animal Ecology},
title = {Annual trends in a natural population of {{\em {T}hrips imaginis}} ({T}hysanoptera)},
year = {1948},
pages = {193--199},
volume = {17},
creationdate = {2012-09-10T00:00:00},
doi = {10.2307/1484},
file = {:Davidson1948.pdf:PDF},
owner = {kingaa},
}
@Article{Dawid1973,
author = {Dawid, A\_P. and Stone, Mervyn and Zidek, James V.},
journal = {Journal of the Royal Statistical Society. Series B},
title = {Marginalization paradoxes in Bayesian and structural inference},
year = {1973},
pages = {189--233},
creationdate = {2014-10-30T00:00:00},
file = {:Dawid1973.pdf:PDF},
modificationdate = {2022-07-20T09:45:25},
owner = {kingaa},
publisher = {JSTOR},
url = {http://www.jstor.org/stable/2984907},
}
@Article{Dennis1996,
author = {Dennis, Brian},
journal = {Ecological Applications},
title = {Should Ecologists Become {B}ayesians?},
year = {1996},
pages = {1095--1103},
volume = {6},
abstract = {Bayesian statistics involve substantial changes in the methods and philosophy of science. Before adopting Bayesian approaches, ecologists should consider carefully whether or not scientific understanding will be enhanced. Frequentist statistical methods, while imperfect, have made an unquestioned contribution to scientific progress and are a workhorse of day-to-day research. Bayesian statistics, by contrast, have a largely untested track record. The papers in this special section on Bayesian statistics exemplify the difficulties inherent in making convincing scientific arguments with Bayesian reasoning.},
creationdate = {2009-09-22T00:00:00},
doi = {10.2307/2269594},
file = {Dennis1996.pdf:Dennis1996.pdf:PDF},
modificationdate = {2022-07-20T09:45:25},
owner = {kingaa},
}
@Article{Dennis2006,
author = {Dennis, Brian and Ponciano, Jos{\'e} Miguel and Lele, Subhash R. and Taper, Mark L. and Staples, David F.},
journal = {Ecological Monographs},
title = {Estimating density dependence, process noise, and observation error},
year = {2006},
number = {3},
pages = {323--341},
volume = {76},
abstract = {We describe a discrete-time, stochastic population model with density dependence, environmental-type process noise, and lognormal observation or sampling error. The model, a stochastic version of the Gompertz model, can be transformed into a linear Gaussian state-space model (Kalman filter) for convenient fitting to time series data. The model has a multivariate normal likelihood function and is simple enough for a variety of uses ranging from theoretical study of parameter estimation issues to routine data analyses in population monitoring. A special case of the model is the discrete-time, stochastic exponential growth model (density independence) with environmental-type process error and lognormal observation error.
We describe two methods for estimating parameters in the Gompertz state-space model, and we compare the statistical qualities of the methods with computer simulations. The methods are maximum likelihood based on observations and restricted maximum likelihood based on first differences. Both offer adequate statistical properties. Because the likelihood function is identical to a repeated-measures analysis of variance model with a random time effect, parameter estimates can be calculated using PROC MIXED of SAS.
We use the model to analyze a data set from the Breeding Bird Survey. The fitted model suggests that over 70% of the noise in the population's growth rate is due to observation error. The model describes the autocovariance properties of the data especially well.
While observation error and process noise variance parameters can both be estimated from one time series, multimodal likelihood functions can and do occur. For data arising from the model, the statistically consistent parameter estimates do not necessarily correspond to the global maximum in the likelihood function. Maximization, simulation, and bootstrapping programs must accommodate the phenomenon of multimodal likelihood functions to produce statistically valid results.},
creationdate = {2014-12-08T00:00:00},
doi = {10.1890/0012-9615(2006)76%5B323:EDDPNA%5D2.0.CO%3B2},
file = {Dennis2006.pdf:Dennis2006.pdf:PDF},
groups = {[kingaa:]},
modificationdate = {2022-07-20T09:45:25},
owner = {kingaa},
publisher = {Eco Soc America},
}
@Book{Deonier2005,
author = {Deonier, Richard C. and Tavar{\'e}, Simon and Waterman, Michael S.},
publisher = {Springer},
title = {Computational Genome Analysis: An Introduction},
year = {2005},
note = {ISBN: 0-387-98785-1},
abstract = {Computational Genome Analysis: An Introduction presents the foundations of key p roblems in computational molecular biology and bioinformatics. It focuses on com putational and statistical principles applied to genomes, and introduces the mat hematics and statistics that are crucial for understanding these applications. A ll computations are done with R.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springeronline.com/0-387-98785-1},
}
@Book{Diggle2006,
author = {Diggle, Peter J. and Ribeiro, Paulo Justiniano},
publisher = {Springer},
title = {Model-based Geostatistics},
year = {2006},
note = {ISBN 0-387-32907-2},
abstract = {Geostatistics is concerned with estimation and prediction problems for spatially continuous phenomena, using data obtained at a limited number of spatial locations. The name reflects its origins in mineral exploration, but the methods are now used in a wide range of settings including public health and the physical and environmental sciences. Model-based geostatistics refers to the application of general statistical principles of modeling and inference to geostatistical problems. This volume is the first book-length treatment of model-based geostatistics.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springer.com/0-387-32907-2},
}
@Book{Dolic2004,
author = {Dolic, Dubravko},
publisher = {R. Oldenbourg},
title = {Statistik mit {R}. Einf\"uhrung f\"ur Wirtschafts- und Sozialwissenschaftler},
year = {2004},
address = {M\"unchen, Wien},
isbn = {3-486-27537-2},
note = {ISBN 3-486-27537-2, in German},
creationdate = {2007-12-31T00:00:00},
language = {de},
modificationdate = {2022-07-20T09:45:23},
owner = {kingaa},
}
@Book{Dudoit2007,
author = {Dudoit, Sandrine and van der Laan, Mark J.},
publisher = {Springer},
title = {Multiple Testing Procedures and Applications to Genomics},
year = {2007},
note = {ISBN: 978-0-387-49316-9},
series = {Springer Series in Statistics},
abstract = {This book provides a detailed account of the theoretical foundations of proposed multiple testing methods and illustrates their application to a range of testing problems in genomics.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springeronline.com/978-0-387-49316-9},
}
@Article{Duncan2000,
author = {Duncan, R. Scot and Duncan, Virginia E.},
journal = {Biotropica},
title = {Forest Succession and Distance from Forest Edge in an Afro-Tropical Grassland},
year = {2000},
number = {1},
pages = {33--41},
volume = {32},
abstract = {Forest succession on degraded tropical lands often is slowed by impoverished seed banks and low rates of seed dispersal. Within degraded landscapes, remnant forests are potential seed sources that could enhance nearby forest succession. The spatial extent that forest can influence succession, however, remains largely unstudied. In abandoned agricultural lands in Kibale National Park, Uganda, recurrent fires have helped perpetuate the dominance of tall (2--3~{m}) grasses. We examined the effects of distance from forest and grassland vegetation structure on succession in a grassland having several years of fire exclusion. At 10 and 25~{m} from forest edge, we quantified vegetation patterns, seed predation, and survival of planted tree seedlings. Natural vegetation was similar at both distances, as was seed (eight species) and seedling (six species) survival; however, distance may be important at spatial or temporal scales not examined in this study. Our results offer insight into forest succession on degraded tropical grasslands following fire exclusion. Naturally recruited trees and tree seedlings were scarce, and seed survival was low (20% after 7~{m}o). While seedling survival was high (95% after 6 to 8~{m}o), seedling shoot growth was very slow ({\={x}}= 0.5~{cm}/100 d), suggesting that survivorship eventually may decline. Recurrent fires often impede forest succession in degraded tropical grasslands; however, even with fire exclusion, our study suggests that forest succession can be very slow, even in close proximity to forest.},
creationdate = {2014-09-14T00:00:00},
doi = {10.1111/j.1744-7429.2000.tb00445.x},
file = {:Duncan2000.pdf:PDF},
groups = {[kingaa:]},
modificationdate = {2022-07-20T09:45:25},
owner = {kingaa},
publisher = {Wiley Online Library},
}
@Article{Dwyer1990,
author = {Dwyer, Greg and Levin, Simon A. and Buttel, Linda},
journal = {Ecological Monographs},
title = {A simulation model of the population dynamics and evolution of myxomatosis},
year = {1990},
month = dec,
number = {4},
pages = {423--447},
volume = {60},
abstract = {Myxoma virus was released into Australia to control the introduced European rabbit, Oryctolagus cuniculus. Within a few years after introduction, the virulence of the virus had declined to an intermediate level, while the resistance of field rabbits and increased sharply. In the nearly 40 yr since the disease was introduced, host resistance has continued to increase, while viral virulence has only recently begun to show signs of counter-increases in some areas. The two questions of interest are thus: Is this system in a coevolutionary arms race (Dawkins and Krebs 1979); that is, will both host and pathogen continue to evolve antagonistically? Will the virus continue to control the rabbit in the future? We present a simulation model based loosely on previous host--pathogen models (Anderson and May 1979), but with detailed accounting of the virus titer in infected hosts, and using realistic estimates of the demographic parameters of the rabbit, including age structure and seasonally varying reproduction. For a single virulence grade, by varying the non-disease (or @'natural@') mortality of the rabbit, the age at first reproduction of the rabbit, and the virulence grade of the virus, we explored the parameter range for which the rabbit population is controlled. For the most prevalent grades of the virus, grades IIIB and IV, the virus can control the rabbit for most realistic values of natural mortality and age at first reproduction. However, control is dependent on both natural mortality and virus virulence. Since natural mortality varies both geographically and seasonally, the usefulness of the virus may vary geographically and seasonally, and management policies must be sensitive to this variation. When competing against several virus strains that together encompass the complete range of virulence seen in the field, a strain of grade IV virulence competitively excludes strains of all other grades. This competitively dominant grade is close to the most prevalent virulence grades seen in the field. We discuss possible mechanisms of coexistence, including local competitive exclusion with global persistence, variability in host resistance, high mutation rates, and trade-offs between within-host and between-host competitive ability. By examining the effects of flea transmission efficiency, we are able to show that, contrary to commonly held belief, whatever effect fleas have upon the outcome of selection on virulence cannot be due to differences in transmission efficiency between fleas and mosquitoes. Finally, by including host resistance, we improve our prediction of the most prevalent grade of virulence. We conclude that control of the rabbit by the virus is likely for the near future, but that until we understand the genetics of resistance in the rabbit and the relationship between resistance and virulence for different grades of virulence, for different grades of virulence, we cannot make a useful prediction of the long-term state of this system.},
creationdate = {2014-09-02T00:00:00},
file = {Dwyer1990.pdf:Dwyer1990.pdf:PDF},
keywords = {biological control, coevolution, disease transmission, epizootiology, myxomatosis, Oryctolagus cuniculus, population dynamics, simulation model, virulence},
modificationdate = {2022-07-20T09:45:25},
owner = {kingaa},
publisher = {JSTOR},
url = {http://www.jstor.org/stable/1943014},
}
@Article{Eisenberg2014,
author = {Eisenberg, Marisa C. and Hayashi, Michael A. L.},
journal = {Mathematical Biosciences},
title = {Determining identifiable parameter combinations using subset profiling.},
year = {2014},
month = aug,
pmid = {25173434},
abstract = {Identifiability is a necessary condition for successful parameter estimation of dynamic system models. A major component of identifiability analysis is determining the identifiable parameter combinations, the functional forms for the dependencies between unidentifiable parameters. Identifiable combinations can help in model reparameterization and also in determining which parameters may be experimentally measured to recover model identifiability. Several numerical approaches to determining identifiability of differential equation models have been developed, however the question of determining identifiable combinations remains incompletely addressed. In this paper, we present a new approach which uses parameter subset selection methods based on the Fisher Information Matrix, together with the profile likelihood, to effectively estimate identifiable combinations. We demonstrate this approach on several example models in pharmacokinetics, cellular biology, and physiology.},
creationdate = {2014-09-16T00:00:00},
doi = {10.1016/j.mbs.2014.08.008},
file = {:Eisenberg2014.pdf:PDF},
groups = {[kingaa:]},
language = {eng},
medline-pst = {aheadofprint},
modificationdate = {2022-07-20T09:45:25},
owner = {kingaa},
pii = {S0025-5564(14)00163-1},
school = {Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, United States. Electronic address: [email protected].},
}
@Article{Ellison2004,
author = {Ellison, Aaron M.},
journal = {Ecology Letters},
title = {Bayesian inference in ecology},
year = {2004},
number = {6},
pages = {509--520},
volume = {7},
creationdate = {2014-10-30T00:00:00},
doi = {10.1111/j.1461-0248.2004.00603.x},
file = {:Ellison2004.pdf:PDF},
modificationdate = {2022-07-20T09:45:25},
owner = {kingaa},
publisher = {Wiley Online Library},
}
@Article{Ellison1996,
author = {Ellison, Aaron M.},
journal = {Ecological Applications},
title = {An introduction to {B}ayesian inference for ecological research and environmental decision-making},
year = {1996},
pages = {1036--1046},
creationdate = {2014-10-30T00:00:00},
doi = {10.2307/2269588},
file = {:Ellison1996.pdf:PDF},
groups = {[kingaa:]},
modificationdate = {2022-07-20T09:45:25},
owner = {kingaa},
publisher = {JSTOR},
}
@Book{Eubank2006,
author = {Eubank, R. L.},
publisher = {Chapman \& Hall/CRC},
title = {A {Kalman} filter primer},
year = {2006},
address = {Boca Raton, Fla},
isbn = {0824723651},
creationdate = {2014-12-08T00:00:00},
owner = {kingaa},
}
@Book{Everitt2006,
author = {Everitt, Brian and Hothorn, Torsten},
publisher = {Chapman \& Hall/CRC},
title = {A Handbook of Statistical Analyses Using R},
year = {2006},
address = {Boca Raton, FL},
note = {ISBN 1-584-88539-4},
abstract = {With emphasis on the use of R and the interpretation of results rather than the theory behind the methods, this book addresses particular statistical techniques and demonstrates how they can be applied to one or more data sets using R. The authors provide a concise introduction to R, including a summary of its most important features. They cover a variety of topics, such as simple inference, generalized linear models, multilevel models, longitudinal data, cluster analysis, principal components analysis, and discriminant analysis. With numerous figures and exercises, A Handbook of Statistical Analysis using R provides useful information for students as well as statisticians and data analysts.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {crcpress.txt},
owner = {kingaa},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C5394&parent_id=&pc=},
url = {http://cran.r-project.org/src/contrib/Descriptions/HSAUR.html},
}
@Book{Everitt2005,
author = {Everitt, Brian S.},
publisher = {Springer},
title = {An R and S-Plus Companion to Multivariate Analysis},
year = {2005},
note = {ISBN 1-85233-882-2},
abstract = {In this book the core multivariate methodology is covered along with some basic theory for each method described. The necessary R and S-Plus code is given for each analysis in the book, with any differences between the two highlighted.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {springer.txt},
owner = {kingaa},
publisherurl = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40109-22-34953445-0,00.html},
url = {http://biostatistics.iop.kcl.ac.uk/publications/everitt/},
}
@Book{Faraway2006,
author = {Faraway, Julian J.},
publisher = {Chapman \& Hall/CRC},
title = {Extending Linear Models with {R}: Generalized Linear, Mixed Effects and Nonparametric Regression Models},
year = {2006},
address = {Boca Raton, FL},
note = {ISBN 1-584-88424-X},
abstract = {This book surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {crcpress.txt},
owner = {kingaa},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C424X&parent_id=&pc=},
url = {http://www.maths.bath.ac.uk/~jjf23/ELM/},
}
@Book{Faraway2004,
author = {Faraway, Julian J.},
publisher = {Chapman \& Hall/CRC},
title = {Linear Models with R},
year = {2004},
address = {Boca Raton, FL},
note = {ISBN 1-584-88425-8},
abstract = {The book focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion of topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {crcpress.txt},
owner = {kingaa},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C4258&parent_id=&pc=},
url = {http://www.maths.bath.ac.uk/~jjf23/LMR/},
}
@Article{Fawcett2012,
author = {Fawcett, Tim W. and Higginson, Andrew D.},
journal = {Proceedings of the National Academy of Sciences of the U.S.A.},
title = {Heavy use of equations impedes communication among biologists.},
year = {2012},
month = jul,
number = {29},
pages = {11735--11739},
pmid = {22733777},
volume = {109},
abstract = {Most research in biology is empirical, yet empirical studies rely fundamentally on theoretical work for generating testable predictions and interpreting observations. Despite this interdependence, many empirical studies build largely on other empirical studies with little direct reference to relevant theory, suggesting a failure of communication that may hinder scientific progress. To investigate the extent of this problem, we analyzed how the use of mathematical equations affects the scientific impact of studies in ecology and evolution. The density of equations in an article has a significant negative impact on citation rates, with papers receiving 28\% fewer citations overall for each additional equation per page in the main text. Long, equation-dense papers tend to be more frequently cited by other theoretical papers, but this increase is outweighed by a sharp drop in citations from nontheoretical papers (35\% fewer citations for each additional equation per page in the main text). In contrast, equations presented in an accompanying appendix do not lessen a paper's impact. Our analysis suggests possible strategies for enhancing the presentation of mathematical models to facilitate progress in disciplines that rely on the tight integration of theoretical and empirical work.},
creationdate = {2014-09-23T00:00:00},
doi = {10.1073/pnas.1205259109},
groups = {[kingaa:]},
keywords = {Biological Evolution; Communication Barriers; Ecology, methods; Information Dissemination, methods; Journal Impact Factor; Mathematics; Models, Statistical},
language = {eng},
medline-pst = {ppublish},
modificationdate = {2022-07-20T09:45:23},
owner = {kingaa},
pii = {1205259109},
school = {School of Biological Sciences, University of Bristol, Bristol BS8 1UG, United Kingdom. [email protected]},
}
@Article{Filipe2001,
author = {Filipe, J. A. and Gibson, G. J.},
journal = {Bulletin of Mathematical Biology},
title = {Comparing approximations to spatio-temporal models for epidemics with local spread.},
year = {2001},
month = jul,
number = {4},
pages = {603--624},
pmid = {11497160},
volume = {63},
abstract = {Analytical methods for predicting and exploring the dynamics of stochastic, spatially interacting populations have proven to have useful application in epidemiology and ecology. An important development has been the increasing interest in spatially explicit models, which require more advanced analytical techniques than the usual mean-field or mass-action approaches. The general principle is the derivation of differential equations describing the evolution of the expected population size and other statistics. As a result of spatial interactions no closed set of equations is obtained. Nevertheless, approximate solutions are possible using closure relations for truncation. Here we review and report recent progress on closure approximations applicable to lattice models with nearest-neighbour interactions, including cluster approximations and elaborations on the pair (or pairwise) approximation. This study is made in the context of an SIS model for plant-disease epidemics introduced in Filipe and Gibson (1998, Studying and approximating spatio-temporal models for epidemic spread and control, Phil. Trans. R. Soc. Lond. B 353, 2153-2162) of which the contact process [Harris, T. E. (1974), Contact interactions on a lattice, Ann. Prob. 2, 969] is a special case. The various methods of approximation are derived and explained and their predictions are compared and tested against simulation. The merits and limitations of the various approximations are discussed. A hybrid pairwise approximation is shown to provide the best predictions of transient and long-term, stationary behaviour over the whole parameter range of the model.},
creationdate = {2008-03-12T00:00:00},
doi = {10.1006/bulm.2001.0234},
keywords = {Cluster Analysis; Ecology; Epidemiologic Methods; Models, Biological; Plant Diseases; Stochastic Processes},
modificationdate = {2022-07-20T09:45:23},
owner = {kingaa},
pii = {S0092-8240(01)90234-4},
school = {Statistics Scotland, Edinburgh, UK. [email protected]},
}
@Article{Fox1995,
author = {Fox, David R. and Ridsdill-Smith, James},
journal = {Oecologia},
title = {Tests for density dependence revisited},
year = {1995},
number = {4},
pages = {435--443},
volume = {103},
creationdate = {2014-09-30T00:00:00},
doi = {10.1007/BF00328681},
file = {:Fox1995.pdf:PDF},
groups = {[kingaa:]},
owner = {kingaa},
publisher = {Springer},
}
@Book{Fox2002,
author = {Fox, John},
publisher = {Sage Publications},
title = {An {R} and {S-Plus} Companion to Applied Regression},
year = {2002},
address = {Thousand Oaks, CA, USA},
note = {ISBN 0-761-92279-2},
abstract = {A companion book to a text or course on applied regression (such as ``Applied Regression, Linear Models, and Related Methods'' by the same author). It introduces S, and concentrates on how to use linear and generalized-linear models in S while assuming familiarity with the statistical methodology.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
owner = {kingaa},
url = {http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/index.html},
}
@Article{Fussman2000,
author = {Fussman, Gregor F. and Ellner, Stephen P. and Sherzer, Kyle W. and Nelson G. Hairston, Jr.},
journal = {Science},
title = {Crossing the {Hopf} Bifurcation in a Live Predator-Prey System},
year = {2000},
pages = {1358--60},
volume = {290},
file = {Fussman2000.pdf:Fussman2000.pdf:PDF},
modificationdate = {2022-07-20T09:45:25},
}
@Article{Gelman2008,
author = {Gelman, Andrew},
journal = {Bayesian Analysis},
title = {Objections to {B}ayesian statistics},
year = {2008},
number = {3},
pages = {445--449},
volume = {3},
creationdate = {2014-10-31T00:00:00},
doi = {10.1214/08-BA318},
file = {:Gelman2008.pdf:PDF},
groups = {[kingaa:]},
owner = {kingaa},
publisher = {International Society for Bayesian Analysis},
}
@Book{Gelman2007,
author = {Gelman, Andrew and Hill, Jennifer},
publisher = {Cambridge University Press},
title = {Data analysis using regression and multilevel/hierarchical models},
year = {2007},
address = {Cambridge},
isbn = {052168689X},
creationdate = {2014-11-24T00:00:00},
owner = {kingaa},
url = {http://www.stat.columbia.edu/~gelman/arm/},
}
@Article{Gelman2002,
author = {Gelman, A. and Nolan, D.},
journal = {American Statistician},
title = {You can load a die, but you can't bias a coin},
year = {2002},
pages = {308--311},
volume = {56},
creationdate = {2008-02-04T00:00:00},
modificationdate = {2022-07-20T09:45:23},
owner = {kingaa},
}
@Article{Gelman2013,
author = {Gelman, Andrew and Shalizi, Cosma Rohilla},
journal = {British Journal of Mathematical and Statistical Psychology},
title = {Philosophy and the practice of {B}ayesian statistics},
year = {2013},
number = {1},
pages = {8--38},
volume = {66},
creationdate = {2014-10-31T00:00:00},
doi = {10.1111/j.2044-8317.2011.02037.x},
file = {:Gelman2013.pdf:PDF},
groups = {[kingaa:]},
owner = {kingaa},
publisher = {Wiley Online Library},
}
@Book{Gentleman2008,
author = {Gentleman, Robert},
publisher = {Chapman \& Hall/CRC},
title = {Bioinformatics with R},
year = {2008},
address = {Boca Raton, FL},
note = {ISBN 1-420-06367-7},
abstract = {The Bioconductor project was initiated in 2001 to provide a resource of R packages that specifically address bioinformatics problems. Written by the leader of this project and the original developer of the R software, this book provides an overview of techniques to develop R programming skills for bioinformatics. The book presents comprehensive coverage of a broad range of key topics, including R language fundamentals, object-oriented programming in R, foreign language interfaces, building R packages, handling different data technologies, and debugging. It includes a number of detailed illustrative bioinformatics examples as well as exercises to demonstrate techniques.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {crcpress.txt},
owner = {kingaa},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C6367},
}
@Article{Gibson2003,
author = {Gibson, G. J.},
journal = {Inverse Problems},
title = {Selecting Bayesian priors for stochastic rates using extended functional models},
year = {2003},
month = apr,
number = {2},
pages = {265--278},
volume = {19},
creationdate = {2008-03-12T00:00:00},
owner = {kingaa},
sn = {0266-5611},
ut = {ISI:000182424100003},
}
@Article{Gibson2001,
author = {Gibson, Gavin J. and Renshaw, Eric},
journal = {Statistics and Computing},
title = {Likelihood estimation for stochastic compartmental models using {Markov} chain methods},
year = {2001},
number = {4},
pages = {347--358},
volume = {11},
abstract = {This paper presents a method for estimating likelihood ratios for stochastic compartment models when only times of removals from a population are observed. The technique operates by embedding the models in a composite model parameterised by an integer k which identifies a switching time when dynamics change from one model to the other. Likelihood ratios can then be estimated from the posterior density of k using Markov chain methods. The techniques are illustrated by a simulation study involving an immigration-death model and validated using analytic results derived for this case. They are also applied to compare the fit of stochastic epidemic models to historical data on a smallpox epidemic. In addition to estimating likelihood ratios, the method can be used for direct estimation of likelihoods by selecting one of the models in the comparison to have a known likelihood for the observations. Some general properties of the likelihoods typically arising in this scenario, and their implications for inference, are illustrated and discussed.},
creationdate = {2008-03-18T00:00:00},
doi = {10.1023/A:1011973120681},
file = {Gibson2001.pdf:Gibson2001.pdf:PDF},
owner = {kingaa},
}
@Article{Gibson2001a,
author = {Gibson, G. J. and Renshaw, E.},
journal = {Inverse Problems},
title = {Inference for immigration-death processes with single and paired immigrants},
year = {2001},
month = jun,
number = {3},
pages = {455--466},
volume = {17},
creationdate = {2008-03-12T00:00:00},
owner = {kingaa},
sn = {0266-5611},
ut = {ISI:000169668700006},
}
@Article{Gibson1998,
author = {Gibson, G. J. and Renshaw, E.},
journal = {IMA Journal of Mathematics Applied in Medicine and Biology},
title = {Estimating parameters in stochastic compartmental models using Markov chain methods},
year = {1998},
number = {1},
pages = {19--40},
volume = {15},
abstract = {Markov chain Monte Carlo methodology is presented for estimating parameters in stochastic compartmental models from incomplete observations of the corresponding Markov process. The methods, which are based on the Metroplis-Hastings algorithm, are developed in the context of epidemic models. Their use is illustrated for the particular case where only susceptible, infective, and removed states are represented using simulated realizations of the process. By comparing estimated likelihoods with theoretical forms, in cases where these can be derived, or with known model parameters, we show that the methods can be used to provide meaningful estimates of parameters and parameter uncertainty. Potential applications of the techniques are also discussed. Keywords:stochastic compartment models; parameter estimation; Markov chain Monte Carlo methods; hidden Markov models.},
creationdate = {2008-03-19T00:00:00},
doi = {10.1093/imammb/15.1.19},
file = {Gibson1998.pdf:Gibson1998.pdf:PDF},
owner = {kingaa},
}
@Article{Gibson2000,
author = {Gibson, M. A. and Bruck, J.},
journal = {Journal of Physical Chemistry A},
title = {Efficient exact stochastic simulation of chemical systems with many species and many channels},
year = {2000},
pages = {1876--1889},
volume = {104},
abstract = {There are two fundamental ways to view coupled systems of chemical equations: as continuous, represented by differential equations whose variables are concentrations, or as discrete, represented by stochastic processes whose variables are numbers of molecules. Although the former is by far more common, systems with very small numbers of molecules are important in some applications (e.g., in small biological cells or in surface processes). In both views, most complicated systems with multiple reaction channels and multiple chemical species cannot be solved analytically. There are exact numerical simulation methods to simulate trajectories of discrete, stochastic systems, (methods that are rigorously equivalent to the Master Equation approach) but these do not scale well to systems with many reaction pathways. This paper presents the Next Reaction Method, an exact algorithm to simulate coupled chemical reactions that is also efficient: it (a) uses only a single random number per simulation event, and (b) takes time proportional to the-logarithm of the number of reactions, not to the number of reactions itself. The Next Reaction Method is extended to include time-dependent rate constants and non-Markov processes and is applied to a sample application in biology (the lysis/lysogeny decision circuit of lambda phage). The performance of the Next Reaction Method on this application is compared with one standard method and an optimized version of that standard method.},
creationdate = {2008-03-18T00:00:00},
file = {Gibson2000.pdf:Gibson2000.pdf:PDF},
modificationdate = {2022-07-20T09:45:25},
owner = {kingaa},
}
@Article{Gompertz1825,
author = {Gompertz, Benjamin},
journal = {Philosophical Transactions of the Royal Society of London},
title = {On the Nature of the Function Expressive of the Law of Human Mortality, and on a New Mode of Determining the Value of Life Contingencies},
year = {1825},
month = jan,
pages = {513--583},
volume = {115},
creationdate = {2014-05-22T00:00:00},
doi = {10.1098/rstl.1825.0026},
file = {:Gompertz1825.pdf:PDF},
groups = {[kingaa:]},
owner = {kingaa},
}
@Manual{Graves2013,
title = {sos: Search contributed R packages, sort by package},
author = {Graves, Spencer and Dorai-Raj, Sundar and Francois, Romain},
note = {R package version 1.3-8},
year = {2013},
modificationdate = {2022-07-20T09:45:23},
url = {http://CRAN.R-project.org/package=sos},
}
@Book{Greenacre2007,
author = {Greenacre, Michael},
publisher = {Chapman \& Hall/CRC},
title = {Correspondence Analysis in Practice, Second Edition},
year = {2007},
address = {Boca Raton, FL},
note = {ISBN 1-584-88616-1},
abstract = {This book shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. T his completely revised, up-to-date edition features a didactic approach with self-contained chapters, extensive marginal notes, informative figure and table captions, and end-of-chapter summaries. It includes a computational appendix that provides the R commands that correspond to most of the analyses featured in the book.},
creationdate = {2007-12-31T00:00:00},
modificationdate = {2022-07-20T09:45:23},
orderinfo = {crcpress.txt},
owner = {kingaa},
publisherurl = {http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=C6161},
}
@Article{Guthery2005,
author = {Guthery, Fred S. and Brennan, Leonard A. and Peterson, Markus J. and Lusk, Jeffrey J.},
journal = {Journal of Wildlife Management},
title = {Invited paper: information theory in wildlife science: critique and viewpoint},
year = {2005},
number = {2},
pages = {457--465},
volume = {69},
creationdate = {2008-01-06T00:00:00},
doi = {10.2193/0022-541X(2005)069[0457:ITIWSC]2.0.CO;2},
modificationdate = {2022-07-20T09:45:23},
owner = {kingaa},
}
@Article{Guthery2004,
author = {Guthery, F. S. and Lusk, J. J. and Peterson, M. J.},
journal = {Wildlife Society Bulletin},
title = {Hypotheses in wildlife science},
year = {2004},
pages = {1325--1332},
volume = {32},
creationdate = {2008-01-06T00:00:00},
modificationdate = {2022-07-20T09:45:23},
owner = {kingaa},
}
@Article{Guthery2001,
author = {Guthery, F. S. and Lusk, J. J. and Peterson, M. J.},
journal = {Journal of Wildlife Management},
title = {The Fall of the Null Hypothesis: Liabilities and Opportunities},
year = {2001},
pages = {379--384},
volume = {65},
creationdate = {2008-01-06T00:00:00},
modificationdate = {2022-07-20T09:45:23},
owner = {kingaa},
}
@Book{Harrell2001,
author = {Harrell, Frank E.},
publisher = {Springer},
title = {Regression Modeling Strategies, with Applications to Linear Models, Survival Analysis and Logistic Regression},
year = {2001},