Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/105329
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dc.titleRelevance weighted likelihood for dependent data
dc.contributor.authorHu, F.
dc.contributor.authorRosenberger, W.F.
dc.contributor.authorZidek, J.V.
dc.date.accessioned2014-10-28T05:14:42Z
dc.date.available2014-10-28T05:14:42Z
dc.date.issued2000
dc.identifier.citationHu, F.,Rosenberger, W.F.,Zidek, J.V. (2000). Relevance weighted likelihood for dependent data. Metrika 51 (3) : 223-243. ScholarBank@NUS Repository.
dc.identifier.issn00261335
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105329
dc.description.abstractThe relevance-weighted likelihood function weights individual contributions to the likelihood according to their relevance for the inferential problem of interest. Consistency and asymptotic normality of the weighted maximum likelihood estimator were previously proved for independent sequences of random variables. We extend these results to apply to dependent sequences, and, in so doing, provide a unified approach to a number of diverse problems in dependent data. In particular, we provide a heretofore unknown approach for dealing with heterogeneity in adaptive designs, and unify the smoothing approach that appears in many foundational papers for independent data. Applications are given in clinical trials, psychophysics experiments, time series models, transition models, and nonparametric regression.
dc.sourceScopus
dc.subjectAdaptive designs
dc.subjectAsymptotic normality
dc.subjectConsistency
dc.subjectGeneralized estimating equations
dc.subjectMartingales
dc.subjectNonparametric regression
dc.subjectSmoothing autoregression model
dc.subjectUrn model
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.sourcetitleMetrika
dc.description.volume51
dc.description.issue3
dc.description.page223-243
dc.identifier.isiutNOT_IN_WOS
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