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Chapman and Hall/Crc Texts in Statistical Science Ser.: Mathematical Statistics by Keith Knight (1999, Hardcover)
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About this product
Product Identifiers
PublisherCRC Press LLC
ISBN-10158488178X
ISBN-139781584881780
eBay Product ID (ePID)13038426380
Product Key Features
Number of Pages504 Pages
Publication NameMathematical Statistics
LanguageEnglish
SubjectProbability & Statistics / General, Probability & Statistics / Bayesian Analysis
Publication Year1999
TypeTextbook
Subject AreaMathematics
AuthorKeith Knight
SeriesChapman and Hall/Crc Texts in Statistical Science Ser.
FormatHardcover
Dimensions
Item Height1.3 in
Item Weight29.7 Oz
Item Length9.3 in
Item Width6.4 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN99-056997
Dewey Edition21
IllustratedYes
Dewey Decimal519.5
Table Of ContentINTRODUCTION TO PROBABILITY Random Experiments Probability Measures Conditional Probability and Independence Random Variables Expected Values RANDOM VECTORS AND JOINT DISTRIBUTIONS Introduction Discrete and Continuous Random Vectors Conditional Distributions Normal Distributions Poisson Processes Generating Random Variables CONVERGENCE OF RANDOM VARIABLES Introduction Convergence in Probability and Distribution WLLN Proving Convergence in Distribution CLT Some Applications Convergence with Probability 1 PRINCIPLES OF POINT ESTIMATION Introduction Statistical Models Sufficiency Point Estimation Substitution Principle Influence Curves Standard Errors Relative Efficiency The Jackknife LIKELIHOOD-BASED ESTIMATION Introduction The Likelihood Function The Likelihood Principle Asymptotics for MLEs Misspecified Models Nonparametric Maximum Likelihood Estimation Numerical Computation Bayesian Estimation OPTIMAL ESTIMATION Decision Theory UMVUEs The Cramér-Rao Lower Bound Asymptotic Efficiency INTERVAL ESTIMATION AND HYPOTHESIS TESTING Confidence Intervals and Regions Highest Posterior Density Regions Hypothesis Testing Likelihood Ratio Tests Other Issues LINEAR AND GENERALIZED LINEAR MODELS Linear Models Estimation Testing Non-Normal Errors Generalized Linear Models Quasi-Likelihood Models GOODNESS OF FIT Introduction Tests Based on the Multinomial Distribution Smooth Goodness of Fit Tests REFERENCES Each chapter also contains a Problems and Complements section
SynopsisTraditional texts in mathematical statistics can seem heavily weighted with optimality theory of the various flavors developed in the 1940s and50s, and not particularly relevant to statistical practice. Mathematical Statistics stands apart. While mathematically rigorous, its focus is on providing a set of useful tools that allow readers to understand the theoretical underpinnings of statistical methodology. Emphasizing inferential procedures within the framework of parametric models, this treatment reaches beyond "nice" mathematics to provide a balanced, practical text that brings life and relevance to a subject so often perceived as irrelevant and dry., Traditional texts in mathematical statistics can seem - to some readers-heavily weighted with optimality theory of the various flavors developed in the 1940s and50s, and not particularly relevant to statistical practice. Mathematical Statistics stands apart from these treatments. While mathematically rigorous, its focus is on providing a set of useful tools that allow students to understand the theoretical underpinnings of statistical methodology. The author concentrates on inferential procedures within the framework of parametric models, but - acknowledging that models are often incorrectly specified - he also views estimation from a non-parametric perspective. Overall, Mathematical Statistics places greater emphasis on frequentist methodology than on Bayesian, but claims no particular superiority for that approach. It does emphasize, however, the utility of statistical and mathematical software packages, and includes several sections addressing computational issues. The result reaches beyond "nice" mathematics to provide a balanced, practical text that brings life and relevance to a subject so often perceived as irrelevant and dry. Features Provides the tools that allow an understanding of the underpinnings of statistical methods Encourages the use of statistical software, which widens the range of problems reader can consider Brings relevance to the subject-shows readers it has much to offer beyond optimality theory Focuses on inferential procedures within the framework of parametric models, but also views estimation from the nonparametric perspective Solutions manual availalbe on crcpress.com, Traditional texts in mathematical statistics can seem - to some readers-heavily weighted with optimality theory of the various flavors developed in the 1940s and50s, and not particularly relevant to statistical practice. Mathematical Statistics stands apart from these treatments. While mathematically rigorous, its focus is on providing a set of useful tools that allow students to understand the theoretical underpinnings of statistical methodology.The author concentrates on inferential procedures within the framework of parametric models, but - acknowledging that models are often incorrectly specified - he also views estimation from a non-parametric perspective. Overall, Mathematical Statistics places greater emphasis on frequentist methodology than on Bayesian, but claims no particular superiority for that approach. It does emphasize, however, the utility of statistical and mathematical software packages, and includes several sections addressing computational issues.The result reaches beyond "nice" mathematics to provide a balanced, practical text that brings life and relevance to a subject so often perceived as irrelevant and dry.
LC Classification NumberQA276.K565 2000
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