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Applied Stochastic Modelling 2nd edition [Minkštas viršelis]

4.00/5 (6 ratings by Goodreads)
(University of Kent, UK)
  • Formatas: Paperback / softback, 368 pages, aukštis x plotis: 234x156 mm, weight: 521 g, 73 Illustrations, black and white
  • Serija: Chapman & Hall/CRC Texts in Statistical Science
  • Išleidimo metai: 02-Dec-2008
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1584886668
  • ISBN-13: 9781584886662
Kitos knygos pagal šią temą:
Applied Stochastic Modelling 2nd editionDidesnis paveikslėlis
  • Formatas: Paperback / softback, 368 pages, aukštis x plotis: 234x156 mm, weight: 521 g, 73 Illustrations, black and white
  • Serija: Chapman & Hall/CRC Texts in Statistical Science
  • Išleidimo metai: 02-Dec-2008
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1584886668
  • ISBN-13: 9781584886662
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781584886662.html
Raktažodžiai:
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.

Recenzijos

Praise for the First Edition

The authors 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 authors 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

Preface to the Second Edition xi
Preface xiii
1 Introduction and Examples 1
1.1 Introduction
1
1.2 Examples of data sets
3
1.3 Discussion
11
1.4 Exercises
12
2 Basic Model-Fitting 17
2.1 Introduction
17
2.2 Maximum-likelihood estimation for a geometric model
17
2.3 Maximum-likelihood for the beta-geometric model
22
2.4 Modelling polyspermy
26
2.5 Which model?
31
2.6 What is a model for?
32
2.7 *Mechanistic models
32
2.8 Discussion
34
2.9 Exercises
34
3 Function Optimisation 45
3.1 Introduction
45
3.2 MATLAB; graphs and finite differences
46
3.3 Deterministic search methods
48
3.4 Stochastic search methods
60
3.5 Accuracy and a hybrid approach
67
3.6 Discussion
68
3.7 Exercises
69
4 Basic Likelihood Tools 77
4.1 Introduction
77
4.2 Estimating standard errors and correlations
80
4.3 Looking at surfaces: profile log-likelihoods
84
4.4 Confidence regions from profiles
89
4.5 Hypothesis testing in model selection
94
4.6 Score and Wald tests
101
4.7 Classical goodness of fit
106
4.8 Model selection bias
106
4.9 Discussion
107
4.10 Exercises
108
5 General Principles 123
5.1 Introduction
123
5.2 Parameterisation
123
5.3 *Parameter redundancy
130
5.4 Boundary estimates
135
5.5 Regression and influence
136
5.6 The EM algorithm
137
5.7 Alternative methods of model-fitting
148
5.8 *Non-regular problems
152
5.9 Discussion
153
5.10 Exercises
154
6 Simulation Techniques 169
6.1 Introduction
169
6.2 Simulating random variables
170
6.3 integral estimation
175
6.4 Verification
177
6.5 *Monte Carlo inference
179
6.6 Estimating sampling distributions
180
6.7 Bootstrap
183
6.8 Monte Carlo testing
190
6.9 Discussion
192
6.10 Exercises
193
7 Bayesian Methods and MCMC 199
7.1 Basic Bayes
199
7.2 Three academic examples
200
7.3 The Gibbs sampler
201
7.4 The Metropolis-Hastings algorithm
213
7.5 A hybrid approach
218
7.6 The data augmentation algorithm
220
7.7 Model probabilities
220
7.8 Model averaging
223
7.9 Reversible jump MCMC: RJMCMC
225
7.10 Discussion
226
7.11 Exercises
228
8 General Families of Models 237
8.1 Common structure
237
8.2 Generalised linear models (GLMs)
237
8.3 Generalised linear mixed models (GLMMs)
245
8.4 Generalised additive models (GAMs)
247
8.5 Discussion
255
8.6 Exercises
256
Index of Data Sets 263
Index of MATLAB Programs 265
Appendix A: Probability and Statistics Reference 269
Appendix B: Computing 275
Appendix C: Kernel Density Estimation 283
Solutions and Comments for Selected Exercises 287
Bibliography 313
Index 333
University of Kent, UK University of Minnesota, Minneapolis, Minnesota, USA Northwestern University, Evanston, Illinois, USA University of British Columbia, Vancouver, Canada