Please use this identifier to cite or link to this item:
|Title:||Relevance weighted likelihood for dependent data||Authors:||Hu, F.
Generalized estimating equations
Smoothing autoregression model
|Issue Date:||2000||Citation:||Hu, F.,Rosenberger, W.F.,Zidek, J.V. (2000). Relevance weighted likelihood for dependent data. Metrika 51 (3) : 223-243. ScholarBank@NUS Repository.||Abstract:||The 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.||Source Title:||Metrika||URI:||http://scholarbank.nus.edu.sg/handle/10635/105329||ISSN:||00261335|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 27, 2022
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.