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Goodness-of-Fit Statistics for Discrete Multivariate Data

Noel A. C. Cressie

Edit� par Springer New York, 2011

ISBN 10 : 1461289319 ISBN 13 : 9781461289319

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - The statistical analysis of discrete multivariate data has received a great deal of attention in the statistics literature over the past two decades. The develop ment ofappropriate models is the common theme of books such as Cox (1970), Haberman (1974, 1978, 1979), Bishop et al. (1975), Gokhale and Kullback (1978), Upton (1978), Fienberg (1980), Plackett (1981), Agresti (1984), Goodman (1984), and Freeman (1987). The objective of our book differs from those listed above. Rather than concentrating on model building, our intention is to describe and assess the goodness-of-fit statistics used in the model verification part of the inference process. Those books that emphasize model development tend to assume that the model can be tested with one of the traditional goodness-of-fit tests 2 2 (e.g., Pearson's X or the loglikelihood ratio G ) using a chi-squared critical value. However, it is well known that this can give a poor approximation in many circumstances. This book provides the reader with a unified analysis of the traditional goodness-of-fit tests, describing their behavior and relative merits as well as introducing some new test statistics. The power-divergence family of statistics (Cressie and Read, 1984) is used to link the traditional test statistics through a single real-valued parameter, and provides a way to consolidate and extend the current fragmented literature. As a by-product of our analysis, a new 2 2 statistic emerges 'between' Pearson's X and the loglikelihood ratio G that has some valuable properties. N� de r�f. du vendeur 9781461289319

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Goodness-of-fit Statistics for Discrete Multivariate Data (Springer Series in Statistics)

Timothy R. C. Read

Edit� par Springer New York, 2012

ISBN 10 : 1461289319 ISBN 13 : 9781461289319

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Paperback. Etat : Brand New. reprint edition. 222 pages. 9.25x6.10x0.50 inches. In Stock. N� de r�f. du vendeur x-1461289319

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Goodness-of-Fit Statistics for Discrete Multivariate Data

Timothy R.C. Read|Noel A.C. Cressie

Edit� par Springer New York, 2011

ISBN 10 : 1461289319 ISBN 13 : 9781461289319

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Goodness-of-Fit Statistics for Discrete Multivariate Data

Read, Timothy R.C.

Edit� par Springer 2011-10, 2011

ISBN 10 : 1461289319 ISBN 13 : 9781461289319

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Goodness-of-Fit Statistics for Discrete Multivariate Data (Springer Series in Statistics)

Read, Timothy R.C., Cressie, Noel A.C.

Edit� par Springer, 2011

ISBN 10 : 1461289319 ISBN 13 : 9781461289319

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Goodness-of-Fit Statistics for Discrete Multivariate Data

Noel A. C. Cressie

Edit� par Springer New York Okt 2011, 2011

ISBN 10 : 1461289319 ISBN 13 : 9781461289319

Neuf Taschenbuch

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The statistical analysis of discrete multivariate data has received a great deal of attention in the statistics literature over the past two decades. The develop ment ofappropriate models is the common theme of books such as Cox (1970), Haberman (1974, 1978, 1979), Bishop et al. (1975), Gokhale and Kullback (1978), Upton (1978), Fienberg (1980), Plackett (1981), Agresti (1984), Goodman (1984), and Freeman (1987). The objective of our book differs from those listed above. Rather than concentrating on model building, our intention is to describe and assess the goodness-of-fit statistics used in the model verification part of the inference process. Those books that emphasize model development tend to assume that the model can be tested with one of the traditional goodness-of-fit tests 2 2 (e.g., Pearson's X or the loglikelihood ratio G ) using a chi-squared critical value. However, it is well known that this can give a poor approximation in many circumstances. This book provides the reader with a unified analysis of the traditional goodness-of-fit tests, describing their behavior and relative merits as well as introducing some new test statistics. The power-divergence family of statistics (Cressie and Read, 1984) is used to link the traditional test statistics through a single real-valued parameter, and provides a way to consolidate and extend the current fragmented literature. As a by-product of our analysis, a new 2 2 statistic emerges 'between' Pearson's X and the loglikelihood ratio G that has some valuable properties. 228 pp. Englisch. N� de r�f. du vendeur 9781461289319

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Goodness-of-Fit Statistics for Discrete Multivariate Data - Couverture souple

Read, Timothy R.C.; Cressie, Noel A.C.

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