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    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:[統計研究所] 期刊論文

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