Regression Modeling With Actuarial and Financial Applications 1st edition by Edward Frees ISBN 0521135966 978-0521135962

Regression Modeling With Actuarial and Financial Applications 1st edition by Edward Frees – Ebook PDF Instant Download/Delivery. 0521135966 978-0521135962
Full download Regression Modeling With Actuarial and Financial Applications 1st edition after payment

Product details:

ISBN 10: 0521135966
ISBN 13: 978-0521135962
Author: Edward Frees 

Statistical techniques can be used to address new situations. This is important in a rapidly evolving risk management and financial world. Analysts with a strong statistical background understand that a large data set can represent a treasure trove of information to be mined and can yield a strong competitive advantage. This book provides budding actuaries and financial analysts with a foundation in multiple regression and time series. Readers will learn about these statistical techniques using data on the demand for insurance, lottery sales, foreign exchange rates, and other applications. Although no specific knowledge of risk management or finance is presumed, the approach introduces applications in which statistical techniques can be used to analyze real data of interest. In addition to the fundamentals, this book describes several advanced statistical topics that are particularly relevant to actuarial and financial practice, including the analysis of longitudinal, two-part (frequency/severity), and fat-tailed data. Datasets with detailed descriptions, sample statistical software scripts in “R” and “SAS,” and tips on writing a statistical report, including sample projects

Regression Modeling With Actuarial and Financial Applications 1st Table of contents:

• Introduction

  • Purpose and Scope of the Book
  • Overview of Regression Techniques in Actuarial and Financial Contexts
  • The Importance of Regression Models in Risk Management and Pricing

• Chapter 1: Basic Regression Concepts

  • Overview of Regression Analysis
  • Types of Regression: Linear, Logistic, and Other Forms
  • Estimating Parameters and Interpreting Coefficients

• Chapter 2: Linear Regression Models

  • Simple Linear Regression
  • Multiple Linear Regression
  • Assumptions of the Linear Model
  • Model Diagnostics and Assumptions Checking

• Chapter 3: Generalized Linear Models (GLMs)

  • Introduction to GLMs
  • Link Functions and Distribution Families
  • Estimation and Inference for GLMs
  • Applications of GLMs in Actuarial Science

• Chapter 4: Time Series and Forecasting

  • Time Series Regression Models
  • Autoregressive and Moving Average Models (ARMA)
  • Seasonal Adjustments and Forecasting Techniques
  • Application in Financial and Actuarial Settings

• Chapter 5: Regression with Censored Data

  • Introduction to Censored Data
  • Survival Analysis and Actuarial Applications
  • Cox Proportional Hazards Model
  • Applications in Insurance and Finance

• Chapter 6: Nonlinear and Nonparametric Regression

  • Polynomial Regression
  • Smoothing Techniques and Nonparametric Methods
  • Regression Trees and Splines
  • When to Use Nonlinear Models

• Chapter 7: Model Selection and Regularization

  • Criteria for Model Selection
  • Stepwise Regression and Lasso
  • Regularization Techniques to Avoid Overfitting
  • Cross-validation and Model Validation Methods

• Chapter 8: Actuarial Applications of Regression

  • Pricing and Reserving in Insurance
  • Risk Assessment and Loss Models
  • Credibility Theory and Model Combining
  • Claims Prediction and Loss Development

• Chapter 9: Financial Applications of Regression

  • Risk Management in Financial Markets
  • Credit Risk Models and Default Prediction
  • Portfolio Optimization and Regression-Based Techniques
  • Actuarial and Financial Modeling in Risk-Based Capital

• Chapter 10: Advanced Topics

  • Mixed-Effects Models and Hierarchical Regression
  • High-Dimensional Data and Machine Learning Techniques
  • Longitudinal Data Analysis
  • Copulas and Dependence Structures in Financial and Actuarial Models

• Chapter 11: Software Implementation

  • Using R and Other Software for Regression Analysis
  • Practical Examples and Code Implementation
  • Interpreting Results from Software Output

• Appendix

  • Mathematical Foundations of Regression
  • Tables of Statistical Distributions
  • Glossary of Terms

• Index

People also search for Regression Modeling With Actuarial and Financial Applications 1st:

regression modeling with actuarial and financial applications edward w. frees

regression modeling with actuarial and financial applications solutions

regression modeling with actuarial and financial applications 2010

regression modeling with actuarial and financial applications fees

regression modeling with actuarial and financial applications pdf