Statistical Inference The Minimum Distance Approach

This book gives a comprehensive account of density-based minimum distance methods and their use in statistical inference. It covers statistical distances, density-based minimum distance methods, discrete and continuous models, asymptotic distributions, robustness, computational issues, residual adju...

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Hlavní autori:	Basu, Ayanendranath, Shioya, Hiroyuki, Park, Chanseok
Médium:	E-kniha Kniha
Jazyk:	English
Vydavateľské údaje:	Boca Raton Chapman and Hall/CRC 2011 CRC Press, Taylor & Francis Group CRC Press LLC Chapman & Hall CRC Press
Vydanie:	1
Edícia:	Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Predmet:	Distances Estimation theory Distance Estimator PD Family Distribution Functions Maximum Likelihood Hellinger Distance Minimum Distance Estimator Power Divergence Statistics Minimum Distance Methods Tuning Parameter Weighted Likelihood Estimators Maximum Likelihood Estimator Null Hypothesis Weighted Likelihood Influence Function Weight Function Minimum Distance Estimation Weighted Likelihood Estimation Kernel Density Estimate Asymptotic Variance Minimum Distance Asymptotic Null Distribution Likelihood Score Function Pearson Residual Density Power Divergence Asymptotic Efficiency
ISBN:	9781420099652, 1420099655
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Obsah:

Front Cover -- Dedication -- Contents -- Preface -- Acknowledgments -- 1. Introduction -- 2. Statistical Distances -- 3. Continuous Models -- 4. Measures of Robustness and Computational Issues -- 5. The Hypothesis Testing Problem -- 6. Techniques for Inlier Modification -- 7. Weighted Likelihood Estimation -- 8. Multinomial Goodness-of-Fit Testing -- 9. The Density Power Divergence -- 10. Other Applications -- 11. Distance Measures in Information and Engineering -- 12. Applications to Other Models -- Bibliography