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Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB
Description
1st Edition
by Russell B. Millar (Author)
This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical paradigm.Key features:
- Provides an accessible introduction to pragmatic maximum likelihood modelling.
- Covers
more advanced topics, including general forms of latent variable models
(including non-linear and non-normal mixed-effects and state-space
models) and the use of maximum likelihood variants, such as estimating
equations, conditional likelihood, restricted likelihood and integrated
likelihood. - Adopts a practical approach, with a focus on
providing the relevant tools required by researchers and practitioners
who collect and analyze real data. - Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology.
- Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB.
- Provides all program code and software extensions on a supporting website.
- Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters.
Details
Year:
2011
Pages:
370
Language:
English
Format:
PDF
Size:
4 MB
ISBN-10:
470094826
ISBN-13:
978-0470094822
ASIN:
B005K04HQS