Noncentral Chi-Square versus Normal Distributions in Describing the Likelihood Ratio Statistic: The Univariate Case and Its Multivariate Implication

Autor/in	Yuan, Ke-Hai
Titel	Noncentral Chi-Square versus Normal Distributions in Describing the Likelihood Ratio Statistic: The Univariate Case and Its Multivariate Implication
Quelle	In: Multivariate Behavioral Research, 43 (2008) 1, S.109-136 (28 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0027-3171
Schlagwörter	Monte Carlo Methods; Graduate Students; Social Sciences; Data Analysis; Statistical Analysis; Sample Size; Probability; Algebra; Calculus; Statistics + Suchen Sie Ihr Suchwort? Monte-Carlo-Methode; Graduate Study; Student; Students; Aufbaustudium; Graduiertenstudium; Hauptstudium; Studentin; Social science; Sozialwissenschaften; Gesellschaftswissenschaften; Auswertung; Statistische Analyse; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Analysis; Differenzialrechnung; Infinitesimalrechnung; Integralrechnung; Statistik
Abstract	In the literature of mean and covariance structure analysis, noncentral chi-square distribution is commonly used to describe the behavior of the likelihood ratio (LR) statistic under alternative hypothesis. Due to the inaccessibility of the rather technical literature for the distribution of the LR statistic, it is widely believed that the noncentral chi-square distribution is justified by statistical theory. Actually, when the null hypothesis is not trivially violated, the noncentral chi-square distribution cannot describe the LR statistic well even when data are normally distributed and the sample size is large. Using the one-dimensional case, this article provides the details showing that the LR statistic asymptotically follows a normal distribution, which also leads to an asymptotically correct confidence interval for the discrepancy between the null hypothesis/model and the population. For each one-dimensional result, the corresponding results in the higher dimensional case are pointed out and references are provided. Examples with real data illustrate the difference between the noncentral chi-square distribution and the normal distribution. Monte Carlo results compare the strength of the normal distribution against that of the noncentral chi-square distribution. The implication to data analysis is discussed whenever relevant. The development is built upon the concepts of basic calculous, linear algebra, and introductory probability and statistics. The aim is to provide the least technical material for quantitative graduate students in social science to understand the condition and limitation of the noncentral chi-square distribution. (Contains 4 tables and 2 footnotes.) (Author).
Anmerkungen	Lawrence Erlbaum. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals/default.html
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10