Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Regression Modeling with Actuarial and Financial Applications
This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.
Longitudinal and Panel Data
An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.
Predictive Modeling Applications in Actuarial Science
This book is for actuaries and financial analysts developing their expertise in statistics and who wish to become familiar with concrete examples of predictive modeling.
Predictive Modeling Applications in Actuarial Science: Volume 2, Case Studies in Insurance
Predictive modeling uses data to forecast future events. It exploits relationships between explanatory variables and the predicted variables from past occurrences to predict future outcomes. Forecasting financial events is a core skill that actuaries routinely apply in insurance and other risk-management applications. Predictive Modeling Applications in Actuarial Science emphasizes life-long learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used to gain a competitive advantage in situations with complex data. Volume 2 examines applications of predictive modeling. Where Volume 1 developed the foundations of predictive modeling, Volume 2 explores practical uses for techniques, focusing on property and casualty insurance. Readers are exposed to a variety of techniques in concrete, real-life contexts that demonstrate their value and the overall value of predictive modeling, for seasoned practicing analysts as well as those just starting out.
Data Analysis Using Regression Models
Designed especially for business and social science students who are familiar with the fundamentals of statistics, this text explores both the theory and practice of regression analysis - proficient in handling the analysis of large data sets. It describes the interaction between data analysis and regression models used to represent the data - to help students learn how to analyze regression data, understand regression models, and how to specify an appropriate model to represent a data set. The main narrative in each chapter stresses application and interpretation of results in applied statistical methods from a user's point of view. Principles are introduced as needed for various applications.
Solutions Manual for Actuarial Mathematics for Life Contingent Risks
"This manual presents solutions to all exercises from Actuarial Mathematics for Life Contingent Risks (AMLCR) by David C.M. Dickson, Mary R. Hardy, Howard Waters; Cambridge University Press, 2009. ISBN 9780521118255"--Pref.
Data Analysis Using Regression and Multilevel/Hierarchical Models
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Constructing Insurable Risk Portfolios
Drawing inspiration from Markowitz portfolio theory, it leverages techniques from probability, statistics, and optimization to build algorithms that construct optimal risk insurable portfolios under budget constraints.
Generalized Linear Models for Insurance Data
This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications. GLMs are used in the insurance industry to support critical decisions. Until now, no text has introduced GLMs in this context or addressed the problems specific to insurance data. Using insurance data sets, this practical, rigorous book treats GLMs, covers all standard exponential family distributions, extends the methodology to correlated data structures, and discusses recent developments which go beyond the GLM. The issues in the book are specific to insurance data, such as model selection in the presence of large data sets and the handling of varying exposure times. Exercises and data-based practicals help readers to consolidate their skills, with solutions and data sets given on the companion website. Although the book is package-independent, SAS code and output examples feature in an appendix and on the website. In addition, R code and output for all the examples are provided on the website.
Insurance Risk and Ruin
Balancing rigor and intuition, the new edition of this first course in risk theory has added exercises and expands on contemporary topics.
