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Thomas A. Severini Likelihood Methods in Statistics (gebunden) Ex Library Good
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eBay-Artikelnr.:135951365280
Artikelmerkmale
- Artikelzustand
- EAN
- 9780198506508
- ISBN
- 9780198506508
- Release Year
- 2000
- Book Title
- Likelihood Methods in Statistics
- ISBN-10
- 0198506503
- Title
- Likelihood Methods in Statistics
- Features
- Ex-Library
- Genre
- Science Nature & Math
- Country/Region of Manufacture
- GB
- Edition
- Illustrated
- Release Date
- 11/09/2000
Über dieses Produkt
Product Identifiers
Publisher
Oxford University Press, Incorporated
ISBN-10
0198506503
ISBN-13
9780198506508
eBay Product ID (ePID)
1665570
Product Key Features
Number of Pages
392 Pages
Publication Name
Likelihood Methods in Statistics
Language
English
Publication Year
2001
Subject
Probability & Statistics / Stochastic Processes, Probability & Statistics / General
Type
Textbook
Subject Area
Mathematics
Series
Oxford Statistical Science Ser.
Format
Hardcover
Dimensions
Item Height
1 in
Item Weight
24.7 Oz
Item Length
9.2 in
Item Width
6.1 in
Additional Product Features
Intended Audience
College Audience
LCCN
00-056537
Dewey Edition
21
Reviews
"This book presents an excellent overview of modern likelihood methods. The presentation is clear and readable enough to be of considerable use to intermediate level graduate students, but is complete and up-to-date enough to act as an excellent first reference for beginning researchers in the field. . . . Moreover, there are excellent discussions of important references as well as numerous exercises. . . . The field of likelihood asymptotics has been undergoing an increasingly rapid development over the last two decades, and this book provides an excellent and detailed account of where the field is, where it came from and perhaps even a bit about where it is going." -- Mathematical Reviews, This book is an excellent account of likelihood-based statistical inference; I believe that it will be a very useful addition to any scholarly library., "This book presents an excellent overview of modern likelihood methods. The presentation is clear and readable enough to be of considerable use to intermediate level graduate students, but is complete and up-to-date enough to act as an excellent first reference for beginning researchers in the field. . . . Moreover, there are excellent discussions of important references as well as numerous exercises. . . . The field of likelihood asymptotics has been undergoing an increasingly rapid development over the last two decades, and this book provides an excellent and detailed account of where the field is, where it came from and perhaps even a bit about where it is going." --Mathematical Reviews
Series Volume Number
22
Illustrated
Yes
Dewey Decimal
519.5/44
Table Of Content
1. Some basic concepts2. Large-sample approximations3. Likelihood4. First-order asymptotic theory5. Higher-order asymptotic theory6. Asymptotic theory and conditional inference7. The signed likelihood ratio statistic8. Likelihood functions for a parameter of interest9. The modified profile likelihood functionAppendix: Data sets used in the examplesReferencesAuthor indexSubject index
Synopsis
This book provides an introduction to the modern theory of likelihood-based statistical inference. This theory is characterized by several important features. One is the recognition that it is desirable to condition on relevant ancillary statistics. Another is that probability approximations are based on saddlepoint and closely related approximations that generally have very high accuracy. A third aspect is that, for models with nuisance parameters, inference isoften based on marginal or conditional likelihoods, or approximations to these likelihoods. These methods have been shown often to yield substantial improvements over classical methods. The bookalso provides an up-to-date account of recent results in the field, which has been undergoing rapid development., This book provides an introduction to the modern theory of likelihood-based statistical inference. This theory is characterized by several important features. One is the recognition that it is desirable to condition on relevant ancillary statistics. Another is that probability approximations are based on saddlepoint and closely related approximations that generally have very high accuracy. A third aspect is that, for models with nuisance parameters, inference is often based on marginal or conditional likelihoods, or approximations to these likelihoods. These methods have been shown often to yield substantial improvements over classical methods. The book also provides an up-to-date account of recent results in the field, which has been undergoing rapid development., This book provides an introduction to the modern theory of likelihood-based statistical inference. This theory is characterized by several important features. One is the recognition that it is desirable to condition on relevant ancillary statistics. Another is that probability approximations are based on saddlepoint and closely related approximations that generally have very high accuracy. A third aspect is that, for models with nuisance parameters, inference is often based on marginal or conditional likelihoods, or approximations to these likelihoods. These methods have been shown often to yield substantial improvements over classical methods. The book also provide an up-to-date account of recent results in the field, which has been undergoing rapid development., Likelihood methods play a central role in statistical theory and methodology. Recently, a new approach to likelihood inference has been developed that often leads to substantial improvements over classical methods. This book gives a detailed introduction to this modern theory of likelihood methods.
LC Classification Number
QA276.8.S38 2000
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