東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Chi-squared goodness of fit tests wi...

Voinov, Vassiliy.

Linked to FindBook

Google Book

Amazon

博客來

Chi-squared goodness of fit tests with applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Chi-squared goodness of fit tests with applications/ V. Voinov, KIMEP University; Institute for Mathematics and Mathematical Modeling of the Ministry of Education and Science, Almaty, Kazakhstan, M. Nikulin, University Bordeaux-2, Bordeaux, France, N. Balakrishnan, McMaster University, Hamilton, Ontario, Canada.
Author:	Voinov, Vassiliy.
other author:	Balakrishnan, N.,
Published:	Amsterdam :Elsevier/AP, : 2013.,
Description:	xii, 229 p. :ill. ;24 cm.
Subject:	Chi-square test. -
Online resource:	http://www.sciencedirect.com/science/book/9780123971944
ISBN:	9780123971944 (electronic bk.)

Chi-squared goodness of fit tests with applications
Voinov, Vassiliy.

Chi-squared goodness of fit tests with applications[electronic resource] /V. Voinov, KIMEP University; Institute for Mathematics and Mathematical Modeling of the Ministry of Education and Science, Almaty, Kazakhstan, M. Nikulin, University Bordeaux-2, Bordeaux, France, N. Balakrishnan, McMaster University, Hamilton, Ontario, Canada. - Amsterdam :Elsevier/AP,2013. - xii, 229 p. :ill. ;24 cm.

Includes bibliographical references (p. 215-226) and index.

"If the number of sample observations n ! 1, the statistic in (1.1) will follow the chi-squared probability distribution with r-1 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

ISBN: 9780123971944 (electronic bk.)

LCCN: 2012039862Subjects--Topical Terms:

2045568
Chi-square test.

LC Class. No.: QA277.3 / .V65 2013

Dewey Class. No.: 519.5/6

Chi-squared goodness of fit tests with applications
LDR:02508cmm a2200229 a 4500 001 1951697
005 20140627153412.0
008 150102s2013 ne a sb 001 0 eng
010 $a 2012039862
020 $a 9780123971944 (electronic bk.)
020 $a 9780123971944
035 $a 14000085
040 $a DLC $b eng $c DLC $e rda $d DLC
041 0 $a eng
042 $a pcc
050 0 0 $a QA277.3 $b .V65 2013
082 0 0 $a 519.5/6 $2 23
100 1 $a Voinov, Vassiliy. $3 2045566
245 1 0 $a Chi-squared goodness of fit tests with applications $h [electronic resource] / $c V. Voinov, KIMEP University; Institute for Mathematics and Mathematical Modeling of the Ministry of Education and Science, Almaty, Kazakhstan, M. Nikulin, University Bordeaux-2, Bordeaux, France, N. Balakrishnan, McMaster University, Hamilton, Ontario, Canada.
260 $a Amsterdam : $b Elsevier/AP, $c 2013.
300 $a xii, 229 p. : $b ill. ; $c 24 cm.
504 $a Includes bibliographical references (p. 215-226) and index.
520 $a "If the number of sample observations n ! 1, the statistic in (1.1) will follow the chi-squared probability distribution with r-1 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 $2 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"-- $c Provided by publisher.
650 0 $a Chi-square test. $3 2045568
650 0 $a Distribution (Probability theory) $3 533179
700 1 $a Balakrishnan, N., $d 1956- $3 533177
700 1 $a Nikulin, M. S. $q (Mikhail Stepanovich) $3 2045567
856 4 0 $u http://www.sciencedirect.com/science/book/9780123971944