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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Basu, Ayanendranath, Shioya, Hiroyuki, Park, Chanseok
Format: E-Book Buch
Sprache:Englisch
Veröffentlicht: Boca Raton Chapman and Hall/CRC 2011
CRC Press, Taylor & Francis Group
CRC Press LLC
Chapman & Hall
CRC Press
Ausgabe:1
Schriftenreihe:Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Schlagworte:
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
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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 adjustment functions, graphical descriptions of robustness, penalized and combined distances, multisample methods, weighted likelihood, and multinomial goodness-of-fit tests. The book also introduces the minimum distance methodology in interdisciplinary areas, such as neural networks and image processing, as well as specialized models and problems, including regression, mixture models, survival and Bayesian analysis, and more.
Bibliographie:Includes bibliographical references (p. 373-402) and index
ISBN:9781420099652
1420099655
DOI:10.1201/b10956