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008 200713s2011 xx o ||||0 eng d
020 9781420099669|q(electronic bk.)
020 |z9781420099652
035 (MiAaPQ)EBC1633203
035 (Au-PeEL)EBL1633203
035 (CaPaEBR)ebr10502499
035 (CaONFJC)MIL331148
035 (OCoLC)740912851
040 MiAaPQ|beng|erda|epn|cMiAaPQ|dMiAaPQ
050 4 QA276.8 -- .B3925 2011eb
082 0 519.54
100 1 Basu, Ayanendranath
245 10 Statistical Inference :|bThe Minimum Distance Approach
250 1st ed
264 1 London :|bCRC Press LLC,|c2011
264 4 |c©2011
300 1 online resource (424 pages)
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
490 1 Chapman and Hall/CRC Monographs on Statistics and Applied
Probability Ser
505 0 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
520 In many ways, estimation by an appropriate minimum
distance method is one of the most natural ideas in
statistics. However, there are many different ways of
constructing an appropriate distance between the data and
the model: the scope of study referred to by "Minimum
Distance Estimation" is literally huge. Filling a
statistical resource gap, Statistical Inference: The
Minimum Distance Approach comprehensively overviews
developments in density-based minimum distance inference
for independently and identically distributed data.
Extensions to other more complex models are also
discussed. Comprehensively covering the basics and
applications of minimum distance inference, this book
introduces and discusses: The estimation and hypothesis
testing problems for both discrete and continuous models
The robustness properties and the structural geometry of
the minimum distance methods The inlier problem and its
possible solutions, and the weighted likelihood estimation
problem The extension of the minimum distance methodology
in interdisciplinary areas, such as neural networks and
fuzzy sets, as well as specialized models and problems,
including semi-parametric problems, mixture models,
grouped data problems, and survival analysis. Statistical
Inference: The Minimum Distance Approach gives a thorough
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, weighted likelihood, and
multinomial goodness-of-fit tests. This carefully crafted
resource is useful to researchers and scientists within
and outside the statistics arena
588 Description based on publisher supplied metadata and other
sources
590 Electronic reproduction. Ann Arbor, Michigan : ProQuest
Ebook Central, 2020. Available via World Wide Web. Access
may be limited to ProQuest Ebook Central affiliated
libraries
650 0 Estimation theory.;Distances
655 4 Electronic books
700 1 Shioya, Hiroyuki
700 1 Park, Chanseok
776 08 |iPrint version:|aBasu, Ayanendranath|tStatistical
Inference : The Minimum Distance Approach|dLondon : CRC
Press LLC,c2011|z9781420099652
830 0 Chapman and Hall/CRC Monographs on Statistics and Applied
Probability Ser
856 40 |uhttps://ebookcentral.proquest.com/lib/sinciatw/
detail.action?docID=1633203|zClick to View