分析相關性資料的普世強韌複合概似函數;Universal Robust Composite Likelihood for General Correlated Data

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/62029

Title:	分析相關性資料的普世強韌複合概似函數;Universal Robust Composite Likelihood for General Correlated Data
Authors:	鄒宗山
Contributors:	國立中央大學統計研究所
Keywords:	數學;統計學
Date:	2014-03-11
Issue Date:	2014-03-11 14:54:14 (UTC+8)
Publisher:	行政院國家科學委員會
Abstract:	研究期間：10308~10407;The method of composite likelihood was introduced by Lindsay (1988) for correlated data. This parametric approach provides consistent regression parameter estimates and can be used to create likelihood ratio test. However, some drawbacks are notable. For example, the large sample composite likelihood ratio test doesn't have the familiar standard chi-square distribution, and no legitimate likelihood function is available by the composite likelihood when model assumption fails. The multivariate negative binomial distribution has been shown to be a superb simple working model for analyzing correlated data. One can operate on the robust negative binomial likelihood function to acquire legitimate likelihood-based inferential tools, such as the likelihood ratio and the score tests and goodness of fit test. There are two goals this research proposal wishes to accomplish. One is to establish composite likelihood using the multivariate negative binomial distribution as the core model. Secondly, we will contrast the normal-based composite likelihood and the negative binomial-based composite likelihood in terms of 1) legitimacy 2) efficiency and 3) simplicity, when the score model assumption fails. According to the plentiful experiences on robust likelihood, we are confident that the latter will be a better choice for correlated data under model misspecifications.
Relation:	財團法人國家實驗研究院科技政策研究與資訊中心
Appears in Collections:	[Graduate Institute of Statistics] Research Project

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