Statistical Inference Based on Divergence Measures
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A01=Leandro Pardo
advanced hypothesis testing
asymptotic
Asymptotic Distribution
Asymptotic Moments
Asymptotic Null Distribution
Author_Leandro Pardo
Category=PBT
Chi Square Test Statistic
Composite Null Hypothesis
Contingency Table
contingency table statistical modeling
discrete data analysis
distribution
entropy-based inference
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
estimator
fisher
Fisher Information Matrix
Gini Simpson Index
information theory applications
kullback
Kullback Leibler Divergence Measure
leibler
likelihood
Likelihood Ratio Test Statistic
Loglinear Models
Marginal Homogeneity
maximum
Maximum Likelihood Estimator
Modified Likelihood Ratio Test Statistic
multivariate statistical methods
Nested Sequence
Null Hypothesis
Null Hypothesis H0
phi-divergence estimators
Pi Log Pi
populations
Power Divergence Family
Power Divergence Test Statistic
Shannon's Entropy
Shannon’s Entropy
Strictly Convex
Test Statistic
Undetermined Lagrangian Multipliers
Wald Test Statistic
weibull
Product details
- ISBN 9781584886006
- Weight: 1110g
- Dimensions: 156 x 234mm
- Publication Date: 10 Oct 2005
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this powerful approach.
Statistical Inference Based on Divergence Measures explores classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence. The first two chapters form an overview, from a statistical perspective, of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations, presenting the interesting possibility of introducing alternative test statistics to classical ones like Wald, Rao, and likelihood ratio. Each chapter concludes with exercises that clarify the theoretical results and present additional results that complement the main discussions.
Clear, comprehensive, and logically developed, this book offers a unique opportunity to gain not only a new perspective on some standard statistics problems, but the tools to put it into practice.
Leandro Pardo
