Robust likelihood inferences for multivariate correlated data

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51878

Title:	Robust likelihood inferences for multivariate correlated data
Authors:	Chen,CH;Tsou,TS
Contributors:	統計研究所
Keywords:	STATISTICAL EVIDENCE;MODELS
Date:	2011
Issue Date:	2012-03-27 19:08:10 (UTC+8)
Publisher:	國立中央大學
Abstract:	Multivariate normal, due to its well-established theories, is commonly utilized to analyze correlated data of various types. However, the validity of the resultant inference is, more often than not, erroneous if the model assumption fails. We present a modification for making the multivariate normal likelihood acclimatize itself to general correlated data. The modified likelihood is asymptotically legitimate for any true underlying joint distributions so long as they have finite second moments. One can, hence, acquire full likelihood inference without knowing the true random mechanisms underlying the data. Simulations and real data analysis are provided to demonstrate the merit of our proposed parametric robust method.
Relation:	JOURNAL OF APPLIED STATISTICS
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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