Applied Stochastic Modelling, Second Edition

Applied Stochastic Modelling, Second Edition

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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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Product details

  • Paperback | 368 pages
  • 152 x 232 x 20mm | 521.63g
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • New edition
  • 2nd New edition
  • 73 Illustrations, black and white
  • 1584886668
  • 9781584886662
  • 1,339,733

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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Review quote

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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About Byron J. T. Morgan

University of Kent, UK University of Minnesota, Minneapolis, Minnesota, USA Northwestern University, Evanston, Illinois, USA University of British Columbia, Vancouver, Canada
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