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dc.contributor.authorWang, Xiaogang
dc.contributor.authorEeden, Constance van
dc.contributor.authorZidek, James V.
dc.date.accessioned2007-03-27T18:33:59Z
dc.date.available2007-03-27T18:33:59Z
dc.date.issued2004
dc.identifier.citationWang, X., van Eeden, C. and Zidek, J.V. (2004). Asymptotic Properties of Weighted Likelihood Estimators.Journal of Statistical Planning and Inference. Vol 119, pp. 37-54.
dc.identifier.issn0378-3758
dc.identifier.urihttp://hdl.handle.net/10315/921
dc.identifier.urihttps://doi.org/10.1016/S0378-3758(02)00410-X
dc.description.abstractThe relevance weighted likelihood method was introduced by Hu and Zidek (Technical Report No. 161, Department of Statistics, The University of British Columbia, Vancouver, BC, Canada, 1995) to formally embrace a variety of statistical procedures for trading bias for precision. Their approach combines all relevant information through a weighted version of the likelihood function. The present paper is concerned with the asymptotic properties of a class of maximum weighted likelihood estimators that contains those considered by Hu and Zidek (Technical Report No. 161, Department of Statistics, The University of British Columbia, Vancouver, BC, Canada, 1995, in: Ahmed, S.E. Reid, N. (Eds.), Empirical Bayes and Likelihood Inference, Springer, New York, 2001, p. 211). Our results complement those of Hu (Can. J. Stat. 25 (1997) 45). In particular, we invoke a di>erent asymptotic paradigm than that in Hu (Can. J. Stat. 25 (1997) 45). Moreover, our adaptive weights are allowed to depend on the data.
dc.language.isoen
dc.publisherJournal of Statistical Planning and Inference
dc.rights© 2004. This manuscript version is made available under the CC-BY-NC-ND 4.0
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAsymptotic normality
dc.subjectConsistency
dc.subjectJames-Stein
dc.subjectWeighted likelihood
dc.titleAsymptotic Properties of Weighted Likelihood Estimators
dc.typeArticle


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© 2004. This manuscript version is made available under the CC-BY-NC-ND 4.0
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