Applied Stochastic ModellingPaperback Chapman & Hall/CRC Texts in Statistical Science
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- Publisher: Chapman & Hall/CRC
- Format: Paperback | 368 pages
- Dimensions: 156mm x 235mm x 20mm | 524g
- Publication date: 2 December 2008
- Publication City/Country: Boca Raton, FL
- ISBN 10: 1584886668
- ISBN 13: 9781584886662
- Edition: 2, Revised
- Edition statement: 2nd Revised edition
- Illustrations note: 73 black & white illustrations
- Sales rank: 956,936
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(R) 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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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
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.