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Title: The asymptotic properties of the maximum-relevance weighted likelihood estimators
Authors: Hu, F. 
Keywords: Asymptotic normality; generalized smoothing model
Kullback-Leibler information
Maximum-likelihood estimate (MLE)
Maximum-relevance weighted likelihood estimate (MREWLE)
Weak consistency
Issue Date: Mar-1997
Citation: Hu, F. (1997-03). The asymptotic properties of the maximum-relevance weighted likelihood estimators. Canadian Journal of Statistics 25 (1) : 45-59. ScholarBank@NUS Repository.
Abstract: We define the maximum-relevance weighted likelihood estimator (MREWLE) using the relevance-weighted likelihood function introduced by Hu and Zidek (1995). Furthermore, we establish the consistency of the MREWLE under a wide range of conditions. Our results generalize those of Wald (1948) to both nonidentically distributed random variables and unequally weighted likelihoods (when dealing with independent data sets of varying relevance to the inferential problem of interest). Asymptotic normality is also proven. Applying these results to generalized smoothing model is discussed.
Source Title: Canadian Journal of Statistics
ISSN: 03195724
Appears in Collections:Staff Publications

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