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Published by Chapman and Hall/CRC, 2011
ISBN 10: 1420099655 ISBN 13: 9781420099652
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Taschenbuch. Condition: Neu. Neuware - 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.
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Published by Chapman and Hall/CRC 2011-07-19, 2011
ISBN 10: 1420099655 ISBN 13: 9781420099652
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Published by Chapman and Hall/CRC, 2011
ISBN 10: 1420099655 ISBN 13: 9781420099652
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Add to basketGebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Ayanendranath Basu, Hiroyuki Shioya, Chanseok ParkIn 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 dist.
Language: English
Published by Chapman and Hall/CRC, 2011
ISBN 10: 1420099655 ISBN 13: 9781420099652
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Buch. Condition: Neu. Statistical Inference | The Minimum Distance Approach | Ayanendranath Basu (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2011 | Chapman and Hall/CRC | EAN 9781420099652 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Language: English
Published by Chapman And Hall/CRC, 2011
ISBN 10: 1420099655 ISBN 13: 9781420099652
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.