From 8096e7f2b5512bf0cde90d2828a5a5cefbd53aab Mon Sep 17 00:00:00 2001 From: Markus Gesmann Date: Wed, 6 Oct 2021 10:40:39 +0100 Subject: [PATCH] Updated URLs --- DESCRIPTION | 9 +++--- man/CLFMdelta.rd | 2 +- man/ChainLadder-package.Rd | 2 +- man/ClarkCapeCod.rd | 2 +- man/ClarkLDF.Rd | 2 +- man/PaidIncurredChain.Rd | 62 ++++++++++++++++++++------------------ man/Table65.rd | 2 +- man/USAA.Rd | 2 +- vignettes/ChainLadder.bib | 12 ++++---- vignettes/NEWS.Rmd | 7 +++-- 10 files changed, 54 insertions(+), 48 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 40667d5d..251f241e 100755 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,8 +2,8 @@ Package: ChainLadder Type: Package Title: Statistical Methods and Models for Claims Reserving in General Insurance -Version: 0.2.12 -Date: 2021-01-05 +Version: 0.2.13 +Date: 2021-10-06 Authors@R: c(person("Markus", "Gesmann", role = c("aut", "cre"), email = "markus.gesmann@googlemail.com"), person("Daniel", "Murphy", role = "aut", @@ -47,7 +47,8 @@ Imports: statmod, cplm (>= 0.7-3), ggplot2, - MASS + MASS, + mvtnorm Suggests: RUnit, knitr, rmarkdown VignetteBuilder: @@ -57,4 +58,4 @@ URL: https://github.com/mages/ChainLadder BugReports: https://github.com/mages/ChainLadder/issues LazyLoad: yes LazyData: yes -RoxygenNote: 6.1.1 +RoxygenNote: 7.1.1 diff --git a/man/CLFMdelta.rd b/man/CLFMdelta.rd index a9bd8af9..80173690 100644 --- a/man/CLFMdelta.rd +++ b/man/CLFMdelta.rd @@ -82,7 +82,7 @@ by convention, } \references{ -\cite{Bardis, Majidi, Murphy. A Family of Chain-Ladder Factor Models for Selected Link Ratios. \emph{Variance}. Pending. Variance 6:2, 2012, pp. 143-160. \url{https://www.variancejournal.org/issues/06-02/143.pdf}} +\cite{Bardis, Majidi, Murphy. A Family of Chain-Ladder Factor Models for Selected Link Ratios. \emph{Variance}. Pending. Variance 6:2, 2012, pp. 143-160.} } \author{ diff --git a/man/ChainLadder-package.Rd b/man/ChainLadder-package.Rd index bdcac911..9e789093 100755 --- a/man/ChainLadder-package.Rd +++ b/man/ChainLadder-package.Rd @@ -41,7 +41,7 @@ \cite{ Zhang, Y. Likelihood-based and Bayesian Methods for Tweedie Compound Poisson Linear Mixed Models, \emph{Statistics and Computing}, forthcoming. } -\cite{Bardis, Majidi, Murphy. A Family of Chain-Ladder Factor Models for Selected Link Ratios. \emph{Variance}. Pending. Variance 6:2, 2012, pp. 143-160. \url{https://www.variancejournal.org/issues/06-02/143.pdf}} +\cite{Bardis, Majidi, Murphy. A Family of Chain-Ladder Factor Models for Selected Link Ratios. \emph{Variance}. Pending. Variance 6:2, 2012, pp. 143-160.} \cite{Modelling the claims development result for solvency purposes. Michael Merz, Mario V. Wüthrich. Casualty Actuarial Society E-Forum, Fall 2008.} diff --git a/man/ClarkCapeCod.rd b/man/ClarkCapeCod.rd index cc544dbc..ec76147a 100644 --- a/man/ClarkCapeCod.rd +++ b/man/ClarkCapeCod.rd @@ -171,7 +171,7 @@ all-lower-case represent observation-level (origin, development age) results.) Clark, David R., "LDF Curve-Fitting and Stochastic Reserving: A Maximum Likelihood Approach", \emph{Casualty Actuarial Society Forum}, Fall, 2003 -\url{https://www.casact.org/pubs/forum/03fforum/03ff041.pdf} +\url{https://www.casact.org/sites/default/files/database/forum_03fforum_03ff041.pdf} } \author{ Daniel Murphy diff --git a/man/ClarkLDF.Rd b/man/ClarkLDF.Rd index c0530da7..79d92f32 100644 --- a/man/ClarkLDF.Rd +++ b/man/ClarkLDF.Rd @@ -167,7 +167,7 @@ all-lower-case represent observation-level (origin, development age) results.) Clark, David R., "LDF Curve-Fitting and Stochastic Reserving: A Maximum Likelihood Approach", \emph{Casualty Actuarial Society Forum}, Fall, 2003 -\url{https://www.casact.org/pubs/forum/03fforum/03ff041.pdf} +\url{ https://www.casact.org/sites/default/files/database/forum_03fforum_03ff041.pdf} } \author{ Daniel Murphy diff --git a/man/PaidIncurredChain.Rd b/man/PaidIncurredChain.Rd index d78e5978..938c4244 100644 --- a/man/PaidIncurredChain.Rd +++ b/man/PaidIncurredChain.Rd @@ -1,4 +1,4 @@ -% Generated by roxygen2 (4.1.1): do not edit by hand +% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PIC.R \name{PaidIncurredChain} \alias{PaidIncurredChain} @@ -18,13 +18,13 @@ The function returns: \item \strong{Ult.Loss} Total ultimate loss. \item \strong{Res.Origin} Claims reserves for different origin years. \item \strong{Res.Tot} Total reserve. - \item \strong{s.e.} Square root of mean square error of prediction + \item \strong{s.e.} Square root of mean square error of prediction for the total ultimate loss. } } \description{ -The Paid-incurred Chain model (Merz, Wuthrich (2010)) combines -claims payments and incurred losses information to get +The Paid-incurred Chain model (Merz, Wuthrich (2010)) combines +claims payments and incurred losses information to get a unified ultimate loss prediction. } \details{ @@ -32,38 +32,41 @@ The method uses some basic properties of multivariate Gaussian distributions to obtain a mathematically rigorous and consistent model for the combination of the two information channels. -We assume as usual that I=J. + +We assume as usual that I=J. The model assumptions for the Log-Normal PIC Model are the following: \itemize{ - \item Conditionally, given \eqn{latex}{\Theta = (\Phi_0,...,\Phi_I, - \Psi_0,...,\Psi_{I-1},\sigma_0,...,\sigma_{I-1},\tau_0,...,\tau_{I-1})} + \item Conditionally, given \eqn{\Theta = (\Phi_0,...,\Phi_I, + \Psi_0,...,\Psi_{I-1},\sigma_0,...,\sigma_{I-1},\tau_0,...,\tau_{I-1})}{\Theta = (\Phi[0],...,\Phi[I], + \Psi[0],...,\Psi[I-1],\sigma[0],...,\sigma[I-1],\tau[0],...,\tau[I-1])} we have \itemize{ - \item the random vector \eqn{latex}{(\xi_{0,0},...,\xi_{I,I}, - \zeta_{0,0},...,\zeta_{I,I-1})} has multivariate Gaussian distribution + \item the random vector \eqn{(\xi_{0,0},...,\xi_{I,I}, + \zeta_{0,0},...,\zeta_{I,I-1})}{(\xi[0,0],...,\xi[I,I], + \zeta[0,0],...,\zeta[I,I-1])} has multivariate Gaussian distribution with uncorrelated components given by - \deqn{latex}{\xi_{i,j} \sim N(\Phi_j,\sigma^2_j),} - \deqn{latex}{\zeta_{k,l} \sim N(\Psi_l,\tau^2_l);} + \deqn{\xi_{i,j} \sim N(\Phi_j,\sigma^2_j),}{\xi[i,j] distributed as N(\Phi[j],\sigma^2[j]),} + \deqn{\zeta_{k,l} \sim N(\Psi_l,\tau^2_l);}{\zeta[k,l] distributed as N(\Psi[l],\tau^2[l]);} \item cumulative payments are given by the recursion - \deqn{latex}{P_{i,j} = P_{i,j-1} \exp(\xi_{i,j}),} - with initial value \eqn{P_{i,0} = \exp (\xi_{i,0})}; - \item incurred losses \eqn{I_{i,j}} are given by the backwards + \deqn{P_{i,j} = P_{i,j-1} \exp(\xi_{i,j}),}{P[i,j] = P[i,j-1] * exp(\xi[i,j]),} + with initial value \eqn{P_{i,0} = \exp (\xi_{i,0})}{P[i,0] = * exp (\xi[i,0])}; + \item incurred losses \eqn{I_{i,j}}{I[i,j]} are given by the backwards recursion - \deqn{latex}{I_{i,j-1} = I_{i,j} \exp(-\zeta_{i,j-1}),} - with initial value \eqn{I_{i,I}=P_{i,I}}. + \deqn{I_{i,j-1} = I_{i,j} \exp(-\zeta_{i,j-1}),}{I[i,j-1] = I[i,j] * exp(-\zeta[i,j-1]),} + with initial value \eqn{I_{i,I}=P_{i,I}}{I[i,I] = P[i,I]}. } - \item The components of \eqn{latex}{\Theta} are indipendent and - \eqn{latex}{\sigma_j,\tau_j > 0} for all j. + \item The components of \eqn{\Theta}{\Theta} are independent and + \eqn{\sigma_j,\tau_j > 0}{\sigma[j],\tau[j] > 0} for all j. } + - -Parameters \eqn{latex}{\Theta} in the model are in general not known and need to be +Parameters \eqn{\Theta}{\Theta} in the model are in general not known and need to be estimated from observations. They are estimated in a Bayesian framework. -In the Bayesian PIC model they assume that the previous assumptions -hold true with deterministic \eqn{latex}{\sigma_0,...,\sigma_J} and -\eqn{latex}{\tau_0,...,\tau_{J-1}} and -\deqn{latex}{\Phi_m \sim N(\phi_m,s^2_m),} -\deqn{latex}{\Psi_n \sim N(\psi_n,t^2_n).} +In the Bayesian PIC model they assume that the previous assumptions +hold true with deterministic \eqn{\sigma_0,...,\sigma_J}{\sigma[0],...,\sigma[J]} and +\eqn{\tau_0,...,\tau_{J-1}}{\tau[0],...,\tau[J-1]} and +\deqn{\Phi_m \sim N(\phi_m,s^2_m),}{\Phi[m] distributed as N(\phi[m],s^2[m]),} +\deqn{\Psi_n \sim N(\psi_n,t^2_n).}{\Psi[n] distributed as N(\psi[n],t^2[n]).} This is not a full Bayesian approach but has the advantage to give analytical expressions for the posterior distributions and the prediction uncertainty. @@ -74,14 +77,13 @@ The model is implemented in the special case of non-informative priors. \examples{ PaidIncurredChain(USAApaid, USAAincurred) } -\author{ -Fabio Concina, \email{fabio.concina@gmail.com} -} \references{ -Merz, M., Wuthrich, M. (2010). Paid-incurred chain claims reserving method. +Merz, M., Wuthrich, M. (2010). Paid-incurred chain claims reserving method. Insurance: Mathematics and Economics, 46(3), 568-579. } \seealso{ \code{\link{MackChainLadder}},\code{\link{MunichChainLadder}} } - +\author{ +Fabio Concina, \email{fabio.concina@gmail.com} +} diff --git a/man/Table65.rd b/man/Table65.rd index fc1684e6..d85925ff 100644 --- a/man/Table65.rd +++ b/man/Table65.rd @@ -30,7 +30,7 @@ A \code{data.frame}. Clark, David R., "LDF Curve-Fitting and Stochastic Reserving: A Maximum Likelihood Approach", \emph{Casualty Actuarial Society Forum}, Fall, 2003 -\url{https://www.casact.org/pubs/forum/03fforum/03ff041.pdf} +\url{ https://www.casact.org/sites/default/files/database/forum_03fforum_03ff041.pdf} } \author{ Daniel Murphy diff --git a/man/USAA.Rd b/man/USAA.Rd index 8bdd105d..f4d1f183 100644 --- a/man/USAA.Rd +++ b/man/USAA.Rd @@ -19,7 +19,7 @@ NAIC Schedule P contains information on claims for major personal and commercial lines for all property-casualty insurers that write business in US. } \source{ -\url{https://www.casact.org/research/index.cfm?fa=loss_reserves_data} +\url{https://www.casact.org} } \references{ CAS website. diff --git a/vignettes/ChainLadder.bib b/vignettes/ChainLadder.bib index c02cc9af..3b76fa12 100755 --- a/vignettes/ChainLadder.bib +++ b/vignettes/ChainLadder.bib @@ -20,8 +20,8 @@ @manual{chainladder Claims Reserving in General Insurance}, author = {Markus Gesmann and Dan Murphy and Wayne Zhang and Alessandro Carrato and Mario W\"{u}thrich and Fabio Concina}, - year = {2020}, - note = {R package version 0.2.11}, + year = {2021}, + note = {R package version 0.2.13}, Howpublished = {\url{https://github.com/mages/ChainLadder}} } @@ -146,7 +146,7 @@ @article{EnglandVerrall1999 @Misc{Schmidt2011, author = {Klaus D. Schmidt}, title = {A Bibliography on Loss Reserving}, - howpublished = {\url{https://www.math.tu-dresden.de/sto/schmidt/dsvm/reserve.pdf}}, + howpublished = {\url{https://tu-dresden.de/mn/math/stochastik/ressourcen/dateien/schmidt/dsvm/reserve.pdf?lang=de}}, year = {2011} } @@ -326,7 +326,7 @@ @Manual{Clark2003 title = {{LDF} Curve-Fitting and Stochastic Reserving: A Maximum Likelihood Approach}, author = {Clark, David R.}, organization = {Casualty Actuarial Society}, - Howpublished = {\url{https://www.casact.org/pubs/forum/03fforum/03ff041.pdf}}, + Howpublished = {\url{https://www.casact.org/sites/default/files/database/forum_03fforum_03ff041.pdf}}, year = {2003}, note = {CAS Fall Forum}, } @@ -582,7 +582,7 @@ @Article{systemfit volume = {23}, number = {4}, pages = {1--40}, - url = {https://www.jstatsoft.org/v23/i04/}, + url = {https://doi.org/10.18637/jss.v023.i04}, } @Article{BaierNeuwirth2007, @@ -991,6 +991,6 @@ @manual{gesmann_morris booktitle = {{CAS} Research Papers}, organization = {Casualty Actuarial Society}, year = {2020}, - howpublished = {\url{https://www.casact.org/research/research-papers/Compartmental-Reserving-Models-GesmannMorris0820.pdf}} + howpublished = {\url{https://www.casact.org/sites/default/files/2021-02/compartmental-reserving-models-gesmannmorris0820.pdf}} } diff --git a/vignettes/NEWS.Rmd b/vignettes/NEWS.Rmd index 21355f4a..9e943b9a 100644 --- a/vignettes/NEWS.Rmd +++ b/vignettes/NEWS.Rmd @@ -17,6 +17,10 @@ vignette: > knitr::opts_chunk$set(echo = TRUE) suppressPackageStartupMessages(library(ChainLadder)) ``` +# Version 0.2.13 [2021-10-06] + + * Updated URLs in the bibliography of the package vignette and help files + # Version 0.2.12 [2021-01-05] * Moved continuous integration testing from TravisCI to GitHub Actions @@ -417,8 +421,7 @@ Fixed tail extrapolation in Vignette. subject to restrictions on the 'selected' factors relative to the input 'Triangle'. See the paper "A Family of Chain-Ladder Factor Models for Selected Link Ratios" - by Bardis, Majidi, Murphy: - https://www.variancejournal.org/issues/?fa=article&abstrID=6943 + by Bardis, Majidi, Murphy, Variance Journal * A new 'coef' method returns the age-to-age factor coefficients of the regression models estimated by the 'chainladder' function.