2nd Edition

Applied Stochastic Modelling

By Byron J.T. Morgan Copyright 2008
    368 Pages 73 B/W Illustrations
    by Chapman & Hall

    368 Pages
    by Chapman & Hall

    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.

    Introduction and Examples


    Examples of data sets

    Basic Model Fitting


    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


    MATLAB: graphs and finite differences

    Deterministic search methods

    Stochastic search methods

    Accuracy and a hybrid approach

    Basic Likelihood Tools


    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



    Parameter redundancy

    Boundary estimates

    Regression and influence

    The EM algorithm

    Alternative methods of model fitting

    Non-regular problems

    Simulation Techniques


    Simulating random variables

    Integral estimation


    Monte Carlo inference

    Estimating sampling distributions


    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



    Discussions and Exercises appear at the end of each chapter.


    Byron J.T. Morgan

    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