The actuar Package. March 24, bstraub... 1 hachemeister... 3 panjer... 4 rearrangepf... 5 simpf Index 8. Buhlmann-Straub Credibility Model
|
|
- Bethany Scott
- 5 years ago
- Views:
Transcription
1 The actuar Package March 24, 2006 Type Package Title Actuarial functions Version Date Author Vincent Goulet, Sébastien Auclair Maintainer Vincent Goulet Collection of functions related to actuarial science applications, namely credibility theory and risk theory, for the moment. The package also includes the famous Hachemeister (1975) data set. Depends R (>= 2.1.0) License GPL version 2 or newer R topics documented: bstraub hachemeister panjer rearrangepf simpf Index 8 bstraub Buhlmann-Straub Credibility Model bstraub calculates credibility premiums in the Bühlmann-Straub credibility model. bstraub(ratios, weights, heterogeneity = c("iterative", "unbiased"), TOL = 1e-06, echo = FALSE) 1
2 2 bstraub Arguments ratios matrix of ratios (contracts in lines, years in columns) weights matrix of weights corresponding to ratios heterogeneity estimator of the between contract heterogeneity parameter used in premium calculation; "iterative" for the Bischel-Straub estimator; "unbiased" for the usual Bühlmann-Straub estimator (see below) TOL Details Value echo maximum relative error in the iterative procedure boolean, whether to echo iterative procedure or not The credibility premium of contract i is given by z i X iw + (1 z i )X zw, where z i = w i â w i â + ŝ 2, X iw is the weighted average of the ratios of contract i, X zw is the weighted average of the matrix of ratios using credibility factors and w i is the total weight of a contract. ŝ 2 is the estimator of the within contract heterogeneity and â is the estimator of the between contract heterogeneity. Missing data are represent by NA in both the matrix of ratios and the matrix of weights. The function can cope with complete lines of NA in case a contract has no experience. A list with the following components: premiums individual collective weights s2 unbiased iterative vector of credibility premiums vector of contract weighted averages collective premium estimator vector of contracts total weights, as used in credibility factors estimator of the within contract heterogeneity parameter unbiased estimator of the between contract heterogeneity parameter iterative estimator of the between contract heterogeneity parameter Estimation of a The Bühlmann-Straub unbiaised estimator (heterogeneity = "unbiased") of the between contracts heterogeneity parameter is ( I ) â = c w i (X iw X ww ) 2 (I 1)ŝ 2, i=1 where c = w /(w 2 I i=1 w2 i ) and I is the number of contracts. The Bishel-Straub pseudo-estimator (heterogeneity = "iterative") is obtained recursively as the solution of â = 1 I 1 I z i (X iw X zw ) 2. The fixed point algorithm is used up, with a relative error of TOL stopping criteria. i=1
3 hachemeister 3 Author(s) Vincent Goulet vincent.goulet@act.ulaval.ca and Sébastien Auclair References Goulet, V. (1998), Principles and Application of Credibility Theory, Journal of Actuarial Practice, Volume 6, ISSN Goovaerts, M. J. and Kaas, R. and van Heerwaarden, A. E. and Bauwelinckx, T. (1990), Effective actuarial methods, North-Holland. Examples data(hachemeister) ## Credibility premiums calculated with the iterative estimator bstraub(hachemeister$claims, hachemeister$weights) ## Credibility premiums calculated with the unbiased estimator bstraub(hachemeister$claims, hachemeister$weights, heterogeneity = "unbiased") hachemeister Hachemeister data set Hachemeister (1975) data set giving average claim amounts in automobile liability insurance in five U.S. states between July 1970 and June 1973 and the corresponding number of claims. data(hachemeister) Format A list with two components: claims a 5 12 matrix of average claim amounts weights a 5 12 matrix of corresponding weights Source Hachemeister, C. A. (1975), Credibility for regression models with application to trend, Proceedings of the Berkeley Actuarial Research Conference on Credibility, Academic Press.
4 4 panjer panjer Panjer s recurison formula Panjer recursion formula to compute the total amount of claims probability function of a portfolio. panjer(fx, freq.dist = c("poisson", "negative binomial", "binomial", "geometric", "logarithmic"),par, p0, TOL = 1e-08, echo= FALSE) Arguments fx freq.dist par p0 TOL echo a vector of the (discretized) claim amount distribution; first element *must* be f X (0) name of the counting distribution named list of the parameter(s) of the counting distribution as they are defined in "rdist". arbitrary amount of probability at zero given to the frequency distribution. It creates zero-modified or zero-truncated distributions stop recursion when cumulative probability function is less than TOL away from 1 print the cumulative distribution of the total amount of claims as it is computed. Details The formula of the (a, b, 1) class is: f S (x) = [p 1 (a + b)p 0 ]f X (x) + min(x,m) y=1 (a + by x )f X(y)f S (x y) 1 af X (0) For the (a, b, 0) class, the result reduces to f S (x) = min(x,m) y=1 (a + by x )f X(y)f S (x y) 1 af X (0) The counting variable is a member of the (a, b, 0) family of discrete distributions if p0 is not specified and a member of the (a,b,1) family if p0 is specified. The logarithmic distribution is a limiting case of the negative binomial distribution where the size parameter is equal to 0. Value A vector representing the probability density function of the total amount of claims. Author(s) Vincent Goulet vincent.goulet@act.ulaval.ca and Sébastien Auclair
5 rearrangepf 5 References Klugman, S.A and Panjer,H.H and Willmot, G.E (2004), Loss Models: from data to decision, Second Edition, Wiley, Sections , 6.6 and Appendix B Examples ### (a,b,0) class with the binomial distribution (a <- panjer(fx=rep(0.5,2), freq.dist="bin",par=list(size=3, prob=0.5),echo=true)) sum(a) ### Example 6.18 of "Loss Models" (Second Edition). ### (a,b,1) class with the Extended Truncated Negative Binomial distribution. (a <- panjer(fx=c(0.3, 0.5, 0.2), "negative bin", par=list(size=0.2, prob=0.25), p0=0, TOL=1E-5 plot(a) rearrangepf Reorganization of a portfolio rearrangepf reorganizes a portfolio of claim amounts stored in a two dimension list. rearrangepf(pf) Arguments pf a two dimension list containing individual claim amounts Details This function takes individual claim amounts of a portfolio of contracts stored in a two dimension list (where each element of the list is a vector of claim amounts) and returns matrices of total claim amounts, number of claims and individual claim amounts. Value A list made of: aggregate frequencies severities matrix of aggregate losses for each contract and each year matrix of the number of claims per contract and per year a list of two elements: claims matrix of the individual claim amounts for the first n - 1 years of observations claims.last matrix of the individual claim amounts for the last year of observations
6 6 simpf Author(s) See Also Vincent Goulet and Sébastien Auclair simpf to create a portfolio of data Examples modelfreq <- list(dist1 = "pois", par1 = list(lambda = quote(lambda * weights)), dist2 = "gamma", par2 = c(shape = 2, rate = 1)) modelsev<-list(dist1 = "lnorm", par1 = list(meanlog = quote(theta), sdlog = 1), dist2 = "norm", par2 = c(mean = 5, sd = 1)) ( x <- simpf(25, 4, modelfreq, modelsev) ) rearrangepf(x$data) simpf Simulation of a portfolio of data simpf simulates a portfolio of data for insurance applications. Both frequency and severity distributions can have an unknown risk parameter that is, they can each be mixtures of models. simpf(contracts, years, model.freq, model.sev, weights) Arguments contracts years model.freq model.sev weights the number of contracts in the porfolio the number of years of experience for each contract named list containing the frequency model (see details below); if NULL, only claim amounts are simulated named list containing the severity model (see details below); if NULL, only claim numbers are simulated a matrix of weights (one per contract and per year) to be used in the simulation of frequencies Details The function allows for continuous mixtures of models for both frequency and severity of losses. The mixing (or risk) parameter is called Lambda in the frequency model and Theta in the severity model. Distribution assumptions are specified using the base name of random number generation functions, e.g. "pois" for the Poisson distribution or "lnorm" for the lognormal. model.freq and model.sev are NULL or named lists composed of:
7 simpf 7 Value dist1 base name of the distribution for a simple model, or of the conditional distribution for a mixed model. par1 named list of the parameters of dist1 as they are defined in rdist1. If needed, the mixing parameter is identified by an unevaluated expression in Lambda and weights for model.freq, or Theta for model.sev. dist2 base name of the mixing distribution, if any. par2 named list of the parameters of dist2 as they are defined in rdist2. A list with two components: data weights a two dimension (contracts rows and years columns) list where each element is a vector of losses, or a matrix if each element has length 1 the matrix of weights given in argument, or a matrix of 1 otherwise. Author(s) Vincent Goulet vincent.goulet@act.ulaval.ca and Sébastien Auclair References See Also Goulet, V. (2006), Credibility for severity revisited, North American Actuarial Journal, to appear. rearrangepf Examples ## Portfolio where both frequency and severity models are mixed. modelfreq <- list(dist1 = "pois", par1 = list(lambda = quote(lambda * weights)), dist2 = "gamma", par2 = c(shape = 2, rate = 1)) modelsev<-list(dist1 = "lnorm", par1 = list(meanlog = quote(theta), sdlog = 1), dist2 = "norm", par2 = c(mean = 5, sd = 1)) data(hachemeister) weights <- hachemeister$weights/mean(hachemeister$weights) simpf(5, 12, modelfreq, modelsev, weights) ## Portfolio where the frequency model is mixed, but not the ## severity model. modelsev <- list(dist1 = "lnorm", par1 = list(meanlog = 7, sd = 1)) simpf(5, 12, modelfreq, modelsev) ## Portofolio with a severity model only and a user function for the ## simulation of claim amounts. rpareto <- function(n, alpha, lambda) lambda * (runif(n)^(-1/alpha) - 1) modelsev <- list(dist1 = "pareto", par1 = list(alpha = 3, lambda = 8000)) simpf(5, 12, model.freq = NULL, modelsev)
8 Index Topic datasets hachemeister, 3 Topic file bstraub, 1 panjer, 4 rearrangepf, 5 simpf, 6 bstraub, 1 hachemeister, 3 panjer, 4 rearrangepf, 5, 7 simpf, 6, 6 8
CAS Course 3 - Actuarial Models
CAS Course 3 - Actuarial Models Before commencing study for this four-hour, multiple-choice examination, candidates should read the introduction to Materials for Study. Items marked with a bold W are available
More informationCambridge University Press Risk Modelling in General Insurance: From Principles to Practice Roger J. Gray and Susan M.
adjustment coefficient, 272 and Cramér Lundberg approximation, 302 existence, 279 and Lundberg s inequality, 272 numerical methods for, 303 properties, 272 and reinsurance (case study), 348 statistical
More informationIntroduction Models for claim numbers and claim sizes
Table of Preface page xiii 1 Introduction 1 1.1 The aim of this book 1 1.2 Notation and prerequisites 2 1.2.1 Probability 2 1.2.2 Statistics 9 1.2.3 Simulation 9 1.2.4 The statistical software package
More informationContents Utility theory and insurance The individual risk model Collective risk models
Contents There are 10 11 stars in the galaxy. That used to be a huge number. But it s only a hundred billion. It s less than the national deficit! We used to call them astronomical numbers. Now we should
More informationSOCIETY OF ACTUARIES EXAM STAM SHORT-TERM ACTUARIAL MATHEMATICS EXAM STAM SAMPLE QUESTIONS
SOCIETY OF ACTUARIES EXAM STAM SHORT-TERM ACTUARIAL MATHEMATICS EXAM STAM SAMPLE QUESTIONS Questions 1-307 have been taken from the previous set of Exam C sample questions. Questions no longer relevant
More information**BEGINNING OF EXAMINATION** A random sample of five observations from a population is:
**BEGINNING OF EXAMINATION** 1. You are given: (i) A random sample of five observations from a population is: 0.2 0.7 0.9 1.1 1.3 (ii) You use the Kolmogorov-Smirnov test for testing the null hypothesis,
More informationBetween the individual and collective models, revisited
Between the individual and collective models, revisited François Dufresne Ecole des HEC University of Lausanne August 14, 2002 Abstract We show that the aggregate claims distribution of a portfolio modelled
More informationChanges to Exams FM/2, M and C/4 for the May 2007 Administration
Changes to Exams FM/2, M and C/4 for the May 2007 Administration Listed below is a summary of the changes, transition rules, and the complete exam listings as they will appear in the Spring 2007 Basic
More informationPackage tailloss. August 29, 2016
Package tailloss August 29, 2016 Title Estimate the Probability in the Upper Tail of the Aggregate Loss Distribution Set of tools to estimate the probability in the upper tail of the aggregate loss distribution
More informationMODELS FOR QUANTIFYING RISK
MODELS FOR QUANTIFYING RISK THIRD EDITION ROBIN J. CUNNINGHAM, FSA, PH.D. THOMAS N. HERZOG, ASA, PH.D. RICHARD L. LONDON, FSA B 360811 ACTEX PUBLICATIONS, INC. WINSTED, CONNECTICUT PREFACE iii THIRD EDITION
More information1. You are given the following information about a stationary AR(2) model:
Fall 2003 Society of Actuaries **BEGINNING OF EXAMINATION** 1. You are given the following information about a stationary AR(2) model: (i) ρ 1 = 05. (ii) ρ 2 = 01. Determine φ 2. (A) 0.2 (B) 0.1 (C) 0.4
More informationExam STAM Practice Exam #1
!!!! Exam STAM Practice Exam #1 These practice exams should be used during the month prior to your exam. This practice exam contains 20 questions, of equal value, corresponding to about a 2 hour exam.
More information[D7] PROBABILITY DISTRIBUTION OF OUTSTANDING LIABILITY FROM INDIVIDUAL PAYMENTS DATA Contributed by T S Wright
Faculty and Institute of Actuaries Claims Reserving Manual v.2 (09/1997) Section D7 [D7] PROBABILITY DISTRIBUTION OF OUTSTANDING LIABILITY FROM INDIVIDUAL PAYMENTS DATA Contributed by T S Wright 1. Introduction
More informationFAV i R This paper is produced mechanically as part of FAViR. See for more information.
The POT package By Avraham Adler FAV i R This paper is produced mechanically as part of FAViR. See http://www.favir.net for more information. Abstract This paper is intended to briefly demonstrate the
More informationSubject CS1 Actuarial Statistics 1 Core Principles. Syllabus. for the 2019 exams. 1 June 2018
` Subject CS1 Actuarial Statistics 1 Core Principles Syllabus for the 2019 exams 1 June 2018 Copyright in this Core Reading is the property of the Institute and Faculty of Actuaries who are the sole distributors.
More informationPackage LNIRT. R topics documented: November 14, 2018
Package LNIRT November 14, 2018 Type Package Title LogNormal Response Time Item Response Theory Models Version 0.3.5 Author Jean-Paul Fox, Konrad Klotzke, Rinke Klein Entink Maintainer Konrad Klotzke
More informationThe Lmoments Package
The Lmoments Package April 12, 2006 Version 1.1-1 Date 2006-04-10 Title L-moments and quantile mixtures Author Juha Karvanen Maintainer Juha Karvanen Depends R Suggests lmomco The
More informationSYLLABUS OF BASIC EDUCATION SPRING 2018 Construction and Evaluation of Actuarial Models Exam 4
The syllabus for this exam is defined in the form of learning objectives that set forth, usually in broad terms, what the candidate should be able to do in actual practice. Please check the Syllabus Updates
More informationSYLLABUS FOR ACTUARIAL TRAINING IN BELGIUM
SYLLABUS FOR ACTUARIAL TRAINING IN BELGIUM ComEd ( KVBA-ARAB) June 2004 The syllabus was approved by the Committee Education during their meeting on Thursday 10 June 2004 as well as by the Board of Directors
More informationEstimating Parameters for Incomplete Data. William White
Estimating Parameters for Incomplete Data William White Insurance Agent Auto Insurance Agency Task Claims in a week 294 340 384 457 680 855 974 1193 1340 1884 2558 9743 Boss, Is this a good representation
More informationPractice Exam 1. Loss Amount Number of Losses
Practice Exam 1 1. You are given the following data on loss sizes: An ogive is used as a model for loss sizes. Determine the fitted median. Loss Amount Number of Losses 0 1000 5 1000 5000 4 5000 10000
More informationMULTIDIMENSIONAL CREDIBILITY MODEL AND ITS APPLICATION
MULTIDIMENSIONAL CREDIBILITY MODEL AND ITS APPLICATION Viera Pacáková, Erik oltés, Bohdan Linda Introduction A huge expansion of a competitive insurance market forms part of the transition process in the
More informationReservePrism Simulation
ReservePrism Simulation For Actuarial Loss Reserving and Pricing This document This document is made from ReservePrism Version 8.3.5.0 Table of Contents Preface... 4 Reserve Prism Simulation Models...
More informationPackage MixedPoisson
Type Package Title Mixed Poisson Models Version 2.0 Date 2016-11-24 Package MixedPoisson December 9, 2016 Author Alicja Wolny-Dominiak and Maintainer Alicja Wolny-Dominiak
More informationSOCIETY OF ACTUARIES/CASUALTY ACTUARIAL SOCIETY EXAM C CONSTRUCTION AND EVALUATION OF ACTUARIAL MODELS EXAM C SAMPLE QUESTIONS
SOCIETY OF ACTUARIES/CASUALTY ACTUARIAL SOCIETY EXAM C CONSTRUCTION AND EVALUATION OF ACTUARIAL MODELS EXAM C SAMPLE QUESTIONS Copyright 2008 by the Society of Actuaries and the Casualty Actuarial Society
More information4-2 Probability Distributions and Probability Density Functions. Figure 4-2 Probability determined from the area under f(x).
4-2 Probability Distributions and Probability Density Functions Figure 4-2 Probability determined from the area under f(x). 4-2 Probability Distributions and Probability Density Functions Definition 4-2
More informationChapter 3 Statistical Quality Control, 7th Edition by Douglas C. Montgomery. Copyright (c) 2013 John Wiley & Sons, Inc.
1 3.1 Describing Variation Stem-and-Leaf Display Easy to find percentiles of the data; see page 69 2 Plot of Data in Time Order Marginal plot produced by MINITAB Also called a run chart 3 Histograms Useful
More informationCredibility. Chapters Stat Loss Models. Chapters (Stat 477) Credibility Brian Hartman - BYU 1 / 31
Credibility Chapters 17-19 Stat 477 - Loss Models Chapters 17-19 (Stat 477) Credibility Brian Hartman - BYU 1 / 31 Why Credibility? You purchase an auto insurance policy and it costs $150. That price is
More informationA Comparison of Stochastic Loss Reserving Methods
A Comparison of Stochastic Loss Reserving Methods Ezgi Nevruz, Yasemin Gençtürk Department of Actuarial Sciences Hacettepe University Ankara/TURKEY 02.04.2014 Ezgi Nevruz (Hacettepe University) Stochastic
More informationVisual fixations and the computation and comparison of value in simple choice SUPPLEMENTARY MATERIALS
Visual fixations and the computation and comparison of value in simple choice SUPPLEMENTARY MATERIALS Ian Krajbich 1 Carrie Armel 2 Antonio Rangel 1,3 1. Division of Humanities and Social Sciences, California
More informationOperational Risk Aggregation
Operational Risk Aggregation Professor Carol Alexander Chair of Risk Management and Director of Research, ISMA Centre, University of Reading, UK. Loss model approaches are currently a focus of operational
More informationAggregation and capital allocation for portfolios of dependent risks
Aggregation and capital allocation for portfolios of dependent risks... with bivariate compound distributions Etienne Marceau, Ph.D. A.S.A. (Joint work with Hélène Cossette and Mélina Mailhot) Luminy,
More information2017 IAA EDUCATION SYLLABUS
2017 IAA EDUCATION SYLLABUS 1. STATISTICS Aim: To enable students to apply core statistical techniques to actuarial applications in insurance, pensions and emerging areas of actuarial practice. 1.1 RANDOM
More informationModern Actuarial Risk Theory
Modern Actuarial Risk Theory Modern Actuarial Risk Theory by Rob Kaas University of Amsterdam, The Netherlands Marc Goovaerts Catholic University of Leuven, Belgium and University of Amsterdam, The Netherlands
More informationCS 361: Probability & Statistics
March 12, 2018 CS 361: Probability & Statistics Inference Binomial likelihood: Example Suppose we have a coin with an unknown probability of heads. We flip the coin 10 times and observe 2 heads. What can
More informationPackage semsfa. April 21, 2018
Type Package Package semsfa April 21, 2018 Title Semiparametric Estimation of Stochastic Frontier Models Version 1.1 Date 2018-04-18 Author Giancarlo Ferrara and Francesco Vidoli Maintainer Giancarlo Ferrara
More informationPROBABILITY. Wiley. With Applications and R ROBERT P. DOBROW. Department of Mathematics. Carleton College Northfield, MN
PROBABILITY With Applications and R ROBERT P. DOBROW Department of Mathematics Carleton College Northfield, MN Wiley CONTENTS Preface Acknowledgments Introduction xi xiv xv 1 First Principles 1 1.1 Random
More informationAustralian Journal of Basic and Applied Sciences. Conditional Maximum Likelihood Estimation For Survival Function Using Cox Model
AENSI Journals Australian Journal of Basic and Applied Sciences Journal home page: wwwajbaswebcom Conditional Maximum Likelihood Estimation For Survival Function Using Cox Model Khawla Mustafa Sadiq University
More informationUPDATED IAA EDUCATION SYLLABUS
II. UPDATED IAA EDUCATION SYLLABUS A. Supporting Learning Areas 1. STATISTICS Aim: To enable students to apply core statistical techniques to actuarial applications in insurance, pensions and emerging
More informationMissing Data. EM Algorithm and Multiple Imputation. Aaron Molstad, Dootika Vats, Li Zhong. University of Minnesota School of Statistics
Missing Data EM Algorithm and Multiple Imputation Aaron Molstad, Dootika Vats, Li Zhong University of Minnesota School of Statistics December 4, 2013 Overview 1 EM Algorithm 2 Multiple Imputation Incomplete
More informationUncertainty-Based Credibility and its Applications
Uncertainty-Based Credibility and its Applications by Pietro Parodi and Stephane Bonche ABSTRACT This paper proposes a methodology to calculate the credibility risk premium based on the uncertainty of
More informationAnalysis of bivariate excess losses
Analysis of bivariate excess losses Ren, Jiandong 1 Abstract The concept of excess losses is widely used in reinsurance and retrospective insurance rating. The mathematics related to it has been studied
More informationInstitute of Actuaries of India Subject CT6 Statistical Methods
Institute of Actuaries of India Subject CT6 Statistical Methods For 2014 Examinations Aim The aim of the Statistical Methods subject is to provide a further grounding in mathematical and statistical techniques
More informationMathematics of Finance Final Preparation December 19. To be thoroughly prepared for the final exam, you should
Mathematics of Finance Final Preparation December 19 To be thoroughly prepared for the final exam, you should 1. know how to do the homework problems. 2. be able to provide (correct and complete!) definitions
More informationExam M Fall 2005 PRELIMINARY ANSWER KEY
Exam M Fall 005 PRELIMINARY ANSWER KEY Question # Answer Question # Answer 1 C 1 E C B 3 C 3 E 4 D 4 E 5 C 5 C 6 B 6 E 7 A 7 E 8 D 8 D 9 B 9 A 10 A 30 D 11 A 31 A 1 A 3 A 13 D 33 B 14 C 34 C 15 A 35 A
More informationOperational Risk Aggregation
Operational Risk Aggregation Professor Carol Alexander Chair of Risk Management and Director of Research, ISMA Centre, University of Reading, UK. Loss model approaches are currently a focus of operational
More informationPackage ratesci. April 21, 2017
Type Package Package ratesci April 21, 2017 Title Confidence Intervals for Comparisons of Binomial or Poisson Rates Version 0.2-0 Date 2017-04-21 Author Pete Laud [aut, cre] Maintainer Pete Laud
More informationContent Added to the Updated IAA Education Syllabus
IAA EDUCATION COMMITTEE Content Added to the Updated IAA Education Syllabus Prepared by the Syllabus Review Taskforce Paul King 8 July 2015 This proposed updated Education Syllabus has been drafted by
More informationMarket Risk Analysis Volume I
Market Risk Analysis Volume I Quantitative Methods in Finance Carol Alexander John Wiley & Sons, Ltd List of Figures List of Tables List of Examples Foreword Preface to Volume I xiii xvi xvii xix xxiii
More informationPackage ensemblemos. March 22, 2018
Type Package Title Ensemble Model Output Statistics Version 0.8.2 Date 2018-03-21 Package ensemblemos March 22, 2018 Author RA Yuen, Sandor Baran, Chris Fraley, Tilmann Gneiting, Sebastian Lerch, Michael
More informationPackage XNomial. December 24, 2015
Type Package Package XNomial December 24, 2015 Title Exact Goodness-of-Fit Test for Multinomial Data with Fixed Probabilities Version 1.0.4 Date 2015-12-22 Author Bill Engels Maintainer
More informationCARe Seminar on Reinsurance - Loss Sensitive Treaty Features. June 6, 2011 Matthew Dobrin, FCAS
CARe Seminar on Reinsurance - Loss Sensitive Treaty Features June 6, 2011 Matthew Dobrin, FCAS 2 Table of Contents Ø Overview of Loss Sensitive Treaty Features Ø Common reinsurance structures for Proportional
More informationPackage optimstrat. September 10, 2018
Type Package Title Choosing the Sample Strategy Version 1.1 Date 2018-09-04 Package optimstrat September 10, 2018 Author Edgar Bueno Maintainer Edgar Bueno
More informationStochastic Claims Reserving _ Methods in Insurance
Stochastic Claims Reserving _ Methods in Insurance and John Wiley & Sons, Ltd ! Contents Preface Acknowledgement, xiii r xi» J.. '..- 1 Introduction and Notation : :.... 1 1.1 Claims process.:.-.. : 1
More informationPackage GCPM. December 30, 2016
Type Package Title Generalized Credit Portfolio Model Version 1.2.2 Date 2016-12-29 Author Kevin Jakob Package GCPM December 30, 2016 Maintainer Kevin Jakob Analyze the
More informationAMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO Academic Press is an Imprint of Elsevier
Computational Finance Using C and C# Derivatives and Valuation SECOND EDITION George Levy ELSEVIER AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO
More informationPricing Excess of Loss Treaty with Loss Sensitive Features: An Exposure Rating Approach
Pricing Excess of Loss Treaty with Loss Sensitive Features: An Exposure Rating Approach Ana J. Mata, Ph.D Brian Fannin, ACAS Mark A. Verheyen, FCAS Correspondence Author: ana.mata@cnare.com 1 Pricing Excess
More informationIntroduction Recently the importance of modelling dependent insurance and reinsurance risks has attracted the attention of actuarial practitioners and
Asymptotic dependence of reinsurance aggregate claim amounts Mata, Ana J. KPMG One Canada Square London E4 5AG Tel: +44-207-694 2933 e-mail: ana.mata@kpmg.co.uk January 26, 200 Abstract In this paper we
More informationIntro to GLM Day 2: GLM and Maximum Likelihood
Intro to GLM Day 2: GLM and Maximum Likelihood Federico Vegetti Central European University ECPR Summer School in Methods and Techniques 1 / 32 Generalized Linear Modeling 3 steps of GLM 1. Specify the
More information2.1 Random variable, density function, enumerative density function and distribution function
Risk Theory I Prof. Dr. Christian Hipp Chair for Science of Insurance, University of Karlsruhe (TH Karlsruhe) Contents 1 Introduction 1.1 Overview on the insurance industry 1.1.1 Insurance in Benin 1.1.2
More informationStatistics & Flood Frequency Chapter 3. Dr. Philip B. Bedient
Statistics & Flood Frequency Chapter 3 Dr. Philip B. Bedient Predicting FLOODS Flood Frequency Analysis n Statistical Methods to evaluate probability exceeding a particular outcome - P (X >20,000 cfs)
More informationPROBLEMS OF WORLD AGRICULTURE
Scientific Journal Warsaw University of Life Sciences SGGW PROBLEMS OF WORLD AGRICULTURE Volume 13 (XXVIII) Number 4 Warsaw University of Life Sciences Press Warsaw 013 Pawe Kobus 1 Department of Agricultural
More informationPackage conf. November 2, 2018
Type Package Package conf November 2, 2018 Title Visualization and Analysis of Statistical Measures of Confidence Version 1.4.0 Maintainer Christopher Weld Imports graphics, stats,
More informationPackage GenOrd. September 12, 2015
Package GenOrd September 12, 2015 Type Package Title Simulation of Discrete Random Variables with Given Correlation Matrix and Marginal Distributions Version 1.4.0 Date 2015-09-11 Author Alessandro Barbiero,
More informationHomework Problems Stat 479
Chapter 10 91. * A random sample, X1, X2,, Xn, is drawn from a distribution with a mean of 2/3 and a variance of 1/18. ˆ = (X1 + X2 + + Xn)/(n-1) is the estimator of the distribution mean θ. Find MSE(
More informationRating Exotic Price Coverage in Crop Revenue Insurance
Rating Exotic Price Coverage in Crop Revenue Insurance Ford Ramsey North Carolina State University aframsey@ncsu.edu Barry Goodwin North Carolina State University barry_ goodwin@ncsu.edu Selected Paper
More informationModelling Premium Risk for Solvency II: from Empirical Data to Risk Capital Evaluation
w w w. I C A 2 0 1 4. o r g Modelling Premium Risk for Solvency II: from Empirical Data to Risk Capital Evaluation Lavoro presentato al 30 th International Congress of Actuaries, 30 marzo-4 aprile 2014,
More informationProxies. Glenn Meyers, FCAS, MAAA, Ph.D. Chief Actuary, ISO Innovative Analytics Presented at the ASTIN Colloquium June 4, 2009
Proxies Glenn Meyers, FCAS, MAAA, Ph.D. Chief Actuary, ISO Innovative Analytics Presented at the ASTIN Colloquium June 4, 2009 Objective Estimate Loss Liabilities with Limited Data The term proxy is used
More informationEstimation of Probability of Defaults (PD) for Low-Default Portfolios: An Actuarial Approach
Estimation of Probability of (PD) for Low-Default s: An Actuarial Approach Nabil Iqbal & Syed Afraz Ali 2012 Enterprise Risk Management Symposium April 18-20, 2012 2012 Nabil, Iqbal and Ali, Syed Estimation
More informationSECOND EDITION. MARY R. HARDY University of Waterloo, Ontario. HOWARD R. WATERS Heriot-Watt University, Edinburgh
ACTUARIAL MATHEMATICS FOR LIFE CONTINGENT RISKS SECOND EDITION DAVID C. M. DICKSON University of Melbourne MARY R. HARDY University of Waterloo, Ontario HOWARD R. WATERS Heriot-Watt University, Edinburgh
More informationHomework Problems Stat 479
Chapter 2 1. Model 1 is a uniform distribution from 0 to 100. Determine the table entries for a generalized uniform distribution covering the range from a to b where a < b. 2. Let X be a discrete random
More informationPackage FADA. May 20, 2016
Type Package Package FADA May 20, 2016 Title Variable Selection for Supervised Classification in High Dimension Version 1.3.2 Date 2016-05-12 Author Emeline Perthame (INRIA, Grenoble, France), Chloe Friguet
More informationModeling. joint work with Jed Frees, U of Wisconsin - Madison. Travelers PASG (Predictive Analytics Study Group) Seminar Tuesday, 12 April 2016
joint work with Jed Frees, U of Wisconsin - Madison Travelers PASG (Predictive Analytics Study Group) Seminar Tuesday, 12 April 2016 claim Department of Mathematics University of Connecticut Storrs, Connecticut
More informationContents. An Overview of Statistical Applications CHAPTER 1. Contents (ix) Preface... (vii)
Contents (ix) Contents Preface... (vii) CHAPTER 1 An Overview of Statistical Applications 1.1 Introduction... 1 1. Probability Functions and Statistics... 1..1 Discrete versus Continuous Functions... 1..
More informationDavid R. Clark. Presented at the: 2013 Enterprise Risk Management Symposium April 22-24, 2013
A Note on the Upper-Truncated Pareto Distribution David R. Clark Presented at the: 2013 Enterprise Risk Management Symposium April 22-24, 2013 This paper is posted with permission from the author who retains
More informationCredit Risk Modeling Using Excel and VBA with DVD O. Gunter Loffler Peter N. Posch. WILEY A John Wiley and Sons, Ltd., Publication
Credit Risk Modeling Using Excel and VBA with DVD O Gunter Loffler Peter N. Posch WILEY A John Wiley and Sons, Ltd., Publication Preface to the 2nd edition Preface to the 1st edition Some Hints for Troubleshooting
More informationPackage SMFI5. February 19, 2015
Type Package Package SMFI5 February 19, 2015 Title R functions and data from Chapter 5 of 'Statistical Methods for Financial Engineering' Version 1.0 Date 2013-05-16 Author Maintainer
More informationLean Six Sigma: Training/Certification Books and Resources
Lean Si Sigma Training/Certification Books and Resources Samples from MINITAB BOOK Quality and Si Sigma Tools using MINITAB Statistical Software A complete Guide to Si Sigma DMAIC Tools using MINITAB Prof.
More informationA Markov Chain Monte Carlo Approach to Estimate the Risks of Extremely Large Insurance Claims
International Journal of Business and Economics, 007, Vol. 6, No. 3, 5-36 A Markov Chain Monte Carlo Approach to Estimate the Risks of Extremely Large Insurance Claims Wan-Kai Pang * Department of Applied
More informationPackage RcmdrPlugin.RiskDemo
Type Package Package RcmdrPlugin.RiskDemo October 3, 2018 Title R Commander Plug-in for Risk Demonstration Version 2.0 Date 2018-10-3 Author Arto Luoma Maintainer R Commander plug-in to demonstrate various
More information2017 IAA EDUCATION GUIDELINES
2017 IAA EDUCATION GUIDELINES 1. An IAA Education Syllabus and Guidelines were approved by the International Forum of Actuarial Associations (IFAA) in June 1998, prior to the creation of the IAA. This
More informationINSTITUTE AND FACULTY OF ACTUARIES. Curriculum 2019 SPECIMEN EXAMINATION
INSTITUTE AND FACULTY OF ACTUARIES Curriculum 2019 SPECIMEN EXAMINATION Subject CS1A Actuarial Statistics Time allowed: Three hours and fifteen minutes INSTRUCTIONS TO THE CANDIDATE 1. Enter all the candidate
More informationPackage finiteruinprob
Type Package Package finiteruinprob December 30, 2016 Title Computation of the Probability of Ruin Within a Finite Time Horizon Version 0.6 Date 2016-12-30 Maintainer Benjamin Baumgartner
More informationDescribing Uncertain Variables
Describing Uncertain Variables L7 Uncertainty in Variables Uncertainty in concepts and models Uncertainty in variables Lack of precision Lack of knowledge Variability in space/time Describing Uncertainty
More informationLoss Simulation Model Testing and Enhancement
Loss Simulation Model Testing and Enhancement Casualty Loss Reserve Seminar By Kailan Shang Sept. 2011 Agenda Research Overview Model Testing Real Data Model Enhancement Further Development Enterprise
More informationWC-5 Just How Credible Is That Employer? Exploring GLMs and Multilevel Modeling for NCCI s Excess Loss Factor Methodology
Antitrust Notice The Casualty Actuarial Society is committed to adhering strictly to the letter and spirit of the antitrust laws. Seminars conducted under the auspices of the CAS are designed solely to
More informationRisk Classification In Non-Life Insurance
Risk Classification In Non-Life Insurance Katrien Antonio Jan Beirlant November 28, 2006 Abstract Within the actuarial profession a major challenge can be found in the construction of a fair tariff structure.
More informationTesting the significance of the RV coefficient
1 / 19 Testing the significance of the RV coefficient Application to napping data Julie Josse, François Husson and Jérôme Pagès Applied Mathematics Department Agrocampus Rennes, IRMAR CNRS UMR 6625 Agrostat
More informationStatistics 431 Spring 2007 P. Shaman. Preliminaries
Statistics 4 Spring 007 P. Shaman The Binomial Distribution Preliminaries A binomial experiment is defined by the following conditions: A sequence of n trials is conducted, with each trial having two possible
More informationNon-pandemic catastrophe risk modelling: Application to a loan insurance portfolio
w w w. I C A 2 0 1 4. o r g Non-pandemic catastrophe risk modelling: Application to a loan insurance portfolio Esther MALKA April 4 th, 2014 Plan I. II. Calibrating severity distribution with Extreme Value
More informationPackage PortfolioOptim
Package PortfolioOptim Title Small/Large Sample Portfolio Optimization Version 1.0.3 April 20, 2017 Description Two functions for financial portfolio optimization by linear programming are provided. One
More informationBackground. opportunities. the transformation. probability. at the lower. data come
The T Chart in Minitab Statisti cal Software Background The T chart is a control chart used to monitor the amount of time between adverse events, where time is measured on a continuous scale. The T chart
More informationyuimagui: A graphical user interface for the yuima package. User Guide yuimagui v1.0
yuimagui: A graphical user interface for the yuima package. User Guide yuimagui v1.0 Emanuele Guidotti, Stefano M. Iacus and Lorenzo Mercuri February 21, 2017 Contents 1 yuimagui: Home 3 2 yuimagui: Data
More informationOn the Distribution and Its Properties of the Sum of a Normal and a Doubly Truncated Normal
The Korean Communications in Statistics Vol. 13 No. 2, 2006, pp. 255-266 On the Distribution and Its Properties of the Sum of a Normal and a Doubly Truncated Normal Hea-Jung Kim 1) Abstract This paper
More informationSTATISTICAL METHODS FOR CATEGORICAL DATA ANALYSIS
STATISTICAL METHODS FOR CATEGORICAL DATA ANALYSIS Daniel A. Powers Department of Sociology University of Texas at Austin YuXie Department of Sociology University of Michigan ACADEMIC PRESS An Imprint of
More informationOptimal Allocation of Policy Limits and Deductibles
Optimal Allocation of Policy Limits and Deductibles Ka Chun Cheung Email: kccheung@math.ucalgary.ca Tel: +1-403-2108697 Fax: +1-403-2825150 Department of Mathematics and Statistics, University of Calgary,
More informationFitting parametric distributions using R: the fitdistrplus package
Fitting parametric distributions using R: the fitdistrplus package M. L. Delignette-Muller - CNRS UMR 5558 R. Pouillot J.-B. Denis - INRA MIAJ user! 2009,10/07/2009 Background Specifying the probability
More informationSTAT 825 Notes Random Number Generation
STAT 825 Notes Random Number Generation What if R/Splus/SAS doesn t have a function to randomly generate data from a particular distribution? Although R, Splus, SAS and other packages can generate data
More informationApproximating a life table by linear combinations of exponential distributions and valuing life-contingent options
Approximating a life table by linear combinations of exponential distributions and valuing life-contingent options Zhenhao Zhou Department of Statistics and Actuarial Science The University of Iowa Iowa
More informationTABLE OF CONTENTS - VOLUME 2
TABLE OF CONTENTS - VOLUME 2 CREDIBILITY SECTION 1 - LIMITED FLUCTUATION CREDIBILITY PROBLEM SET 1 SECTION 2 - BAYESIAN ESTIMATION, DISCRETE PRIOR PROBLEM SET 2 SECTION 3 - BAYESIAN CREDIBILITY, DISCRETE
More information