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Applied Stochastic Modelling: Chapman & Hall/CRC Texts in Statistical Science

Autor Byron J.T. Morgan
en Limba Engleză Hardback – 6 oct 2017
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 EditionAn extended discussion on Bayesian methodsA large number of new exercises A new appendix on computational methodsThe 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.comContinuing 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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Specificații

ISBN-13: 9781138469693
ISBN-10: 1138469696
Pagini: 368
Dimensiuni: 178 x 254 x 26 mm
Greutate: 0.45 kg
Ediția:2
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Texts in Statistical Science


Public țintă

Undergraduate

Recenzii

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

Cuprins

Introduction and Examples. Basic Model Fitting. Function Optimisation. Basic Likelihood Tools. General Principles. Simulation Techniques. Bayesian Methods and MCMC. General Families of Models. Index of Data Sets. Index of MATLAB Programs. Appendices. Solutions and Comments for Selected Exercises. Bibliography. Index.

Descriere

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. This work provides the option of using powerful computational tools for stochastic modelling.