Journal of Statistical Planning and Inference PDF Download
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Author: North-Holland Publishing Company
Publisher:
ISBN:
Category :
Languages : en
Pages : 1304
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Book Description
Author: North-Holland Publishing Company
Publisher:
ISBN:
Category :
Languages : en
Pages : 1304
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category : Electronic journals
Languages : en
Pages : 730
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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 862
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Book Description
Author: Subir Ghosh
Publisher: Wiley
ISBN: 9781119962786
Category : Science
Languages : en
Pages : 512
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Book Description
This book introduces statistical planning and inference, presenting both classical theory and the major developments in the field. Each chapter presents problems and their solutions along with illustrative examples to introduce concepts and methods, and is supported by a supplementary website featuring guidance on how to implement methods using R.
Author: Elsevier Science (Firm)
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: K. Hinkelmann
Publisher:
ISBN:
Category :
Languages : en
Pages : 262
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Author: Elsevier Science (Firm)
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages :
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Author: Ishwar V. Basawa
Publisher:
ISBN:
Category :
Languages : en
Pages : 217
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Book Description
Author: Robert Bartoszynski
Publisher: John Wiley & Sons
ISBN: 9780470191583
Category : Mathematics
Languages : en
Pages : 672
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Book Description
Now updated in a valuable new edition—this user-friendly book focuses on understanding the "why" of mathematical statistics Probability and Statistical Inference, Second Edition introduces key probability and statis-tical concepts through non-trivial, real-world examples and promotes the developmentof intuition rather than simple application. With its coverage of the recent advancements in computer-intensive methods, this update successfully provides the comp-rehensive tools needed to develop a broad understanding of the theory of statisticsand its probabilistic foundations. This outstanding new edition continues to encouragereaders to recognize and fully understand the why, not just the how, behind the concepts,theorems, and methods of statistics. Clear explanations are presented and appliedto various examples that help to impart a deeper understanding of theorems and methods—from fundamental statistical concepts to computational details. Additional features of this Second Edition include: A new chapter on random samples Coverage of computer-intensive techniques in statistical inference featuring Monte Carlo and resampling methods, such as bootstrap and permutation tests, bootstrap confidence intervals with supporting R codes, and additional examples available via the book's FTP site Treatment of survival and hazard function, methods of obtaining estimators, and Bayes estimating Real-world examples that illuminate presented concepts Exercises at the end of each section Providing a straightforward, contemporary approach to modern-day statistical applications, Probability and Statistical Inference, Second Edition is an ideal text for advanced undergraduate- and graduate-level courses in probability and statistical inference. It also serves as a valuable reference for practitioners in any discipline who wish to gain further insight into the latest statistical tools.
Author: Nils Lid Hjort
Publisher: Cambridge University Press
ISBN: 1139484605
Category : Mathematics
Languages : en
Pages : 309
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Book Description
Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
