Applied Stochastic Modelling / Edition 2

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Overview

Highlighting modern computational methods, Applied Stochastic Modelling, Second Edition provides students with the practical experience of scientific computing in applied statistics through a range of interesting real-world applications. It also successfully revises standard probability and statistical theory. Along with an updated bibliography and improved figures, this edition offers numerous updates throughout.

New to the Second Edition

  • An extended discussion on Bayesian methods
  • A large number of new exercises
  • A new appendix on computational methods

The book covers both contemporary and classical aspects of statistics, including survival analysis, Kernel density estimation, Markov chain Monte Carlo, hypothesis testing, regression, bootstrap, and generalised linear models. Although the book can be used without reference to computational programs, the author provides the option of using powerful computational tools for stochastic modelling. All of the data sets and MATLAB® and R programs found in the text as well as lecture slides and other ancillary material are available for download at www.crcpress.com

Continuing in the bestselling tradition of its predecessor, this textbook remains an excellent resource for teaching students how to fit stochastic models to data.

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Editorial Reviews

From the Publisher
Praise for the First Edition

The author’s enthusiasm for his subject shines through this book. There are plenty of interesting example data sets … The book covers much ground in quite a short space … In conclusion, I like this book and strongly recommend it. It covers many of my favourite topics. In another life, I would have liked to have written it, but Professor Morgan has made a better job if it than I would have done.
—Tim Auton, Journal of the Royal Statistical Society

I am seriously considering adopting Applied Stochastic Modelling for a graduate course in statistical computation that our department is offering next term.
—Jim Albert, Journal of the American Statistical Association

…very well written, fresh in its style, with lots of wonderful examples and problems.
—R.P. Dolrow, Technometrics

A useful tool for both applied statisticians and stochastic model users of other fields, such as biologists, sociologists, geologists, and economists.
Zentralblatt MATH

The book is a delight to read, reflecting the author’s enthusiasm for the subject and his wide experience. The layout and presentation of material are excellent. Both for new research students and for experienced researchers needing to update their skills, this is an excellent text and source of reference.
Statistical Methods in Medical Research

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Product Details

Table of Contents

Introduction and Examples

Introduction

Examples of data sets

Basic Model Fitting

Introduction

Maximum-likelihood estimation for a geometric model

Maximum-likelihood for the beta-geometric model

Modelling polyspermy

Which model?

What is a model for?

Mechanistic models

Function Optimisation

Introduction

MATLAB: graphs and finite differences

Deterministic search methods

Stochastic search methods

Accuracy and a hybrid approach

Basic Likelihood Tools

Introduction

Estimating standard errors and correlations

Looking at surfaces: profile log-likelihoods

Confidence regions from profiles

Hypothesis testing in model selection

Score and Wald tests

Classical goodness of fit

Model selection bias

General Principles

Introduction

Parameterisation

Parameter redundancy

Boundary estimates

Regression and influence

The EM algorithm

Alternative methods of model fitting

Non-regular problems

Simulation Techniques

Introduction

Simulating random variables

Integral estimation

Verification

Monte Carlo inference

Estimating sampling distributions

Bootstrap

Monte Carlo testing

Bayesian Methods and MCMC

Basic Bayes

Three academic examples

The Gibbs sampler

The Metropolis–Hastings algorithm

A hybrid approach

The data augmentation algorithm

Model probabilities

Model averaging

Reversible jump MCMC (RJMCMC)

General Families of Models

Common structure

Generalised linear models (GLMs)

Generalised linear mixed models (GLMMs)

Generalised additive models (GAMs)

Index of Data Sets

Index of MATLAB Programs

Appendix A: Probability and Statistics Reference
Appendix B: Computing
Appendix C: Kernel Density Estimation

Solutions and Comments for Selected Exercises

Bibliography

Index

Discussions and Exercises appear at the end of each chapter.

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