 Minimum $$K_{\phi }$$ K ϕ -divergence estimators for multinomial models and applicationsM. Jiménez-Gamero (), R. Pino-Mejías () and A. Rufián-Lizana () Computational Statistics, 2014, vol. 29, issue 1, 363-401 Abstract: The properties of minimum $$K_{\phi }$$ K ϕ -divergence estimators for parametric multinomial populations are well-known when the assumed parametric model is true, namely, they are consistent and asymptotically normally distributed. Here we study these properties when the parametric model is not assumed to be correctly specified. Under certain conditions, these estimators are shown to converge to a well-defined limit and, suitably normalized, they are also asymptotically normal. Two applications of the results obtained are reported. First, two consistent bootstrap estimators of the null distribution of the test statistics in a certain class of goodness-of-fit tests are proposed and studied. Second, two methods for the model selection test problem based on $$K_{\phi }$$ K ϕ -divergence type statistics are proposed and studied. Both applications are illustrated with numerical examples. Copyright Springer-Verlag Berlin Heidelberg 2014 Keywords: Minimum $$K_{\phi }$$ K ϕ -divergence estimator; Consistency; Asymptotic normality; Goodness-of-fit; Bootstrap distribution estimator; Model selection (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:29:y:2014:i:1:p:363-401 Ordering information: This journal article can be ordered from DOI: 10.1007/s00180-013-0452-3 Access Statistics for this article Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik More articles in Computational Statistics from Springer |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.