Chisquared goodness of fit tests with applications
 Author/Creator
 Voinov, Vassiliy.
 Language
 English.
 Edition
 First edition.
 Publication
 Amsterdam : Academic Press/Elsevier, [2013]
 Copyright notice
 ©2013
 Physical description
 xii, 229 pages : illustrations ; 24 cm
Access
Available online
 www.sciencedirect.com ScienceDirect
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QA277.3 .V65 2013

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QA277.3 .V65 2013
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Contents/Summary
 Bibliography
 Includes bibliographical references (p. 215226) and index.
 Summary
 "If the number of sample observations n ! 1, the statistic in (1.1) will follow the chisquared probability distribution with r1 degrees of freedom. We know that this remarkable result is true only for a simple null hypothesis when a hypothetical distribution is specified uniquely (i.e., the parameter is considered to be known). Until 1934, Pearson believed that the limiting distribution of the statistic in (1.1) will be the same if the unknown parameters of the null hypothesis are replaced by their estimates based on a sample; see, for example, Baird (1983), Plackett (1983, p. 63), Lindley (1996), Rao (2002), and Stigler (2008, p. 266). In this regard, it is important to reproduce the words of Plackett (1983, p. 69) concerning E. S. Pearson's opinion: "I knew long ago that KP (meaning Karl Pearson) used the 'correct' degrees of freedom for (a) difference between two samples and (b) multiple contingency tables. But he could not see that in curve fitting should be got asymptotically into the same category." Plackett explained that this crucial mistake of Pearson arose from to Karl Pearson's assumption "that individual normality implies joint normality." Stigler (2008) noted that this error of Pearson "has left a positive and lasting negative impression upon the statistical world." Fisher (1924) clearly showed 1 2 CHAPTER 1. A HISTORICAL ACCOUNT that the number of degrees of freedom of Pearson's test must be reduced by the number of parameters estimated from the sample" Provided by publisher.
Subjects
Bibliographic information
 Publication date
 2013
 Copyright date
 2013
 Responsibility
 V. Voinov, M. Nikulin, N. Balakrishnan.
 ISBN
 9780123971944
 0123971942