More on the Applicability of the Robust Likelihood Methodology

• Main Authors: Chung-Wei Shen, 沈仲維
• Other Authors: Tsung-Shan Tsou
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/01068109258905040525

博士 === 國立中央大學 === 統計研究所 === 97 === In this thesis, we first introduce the idea of robust likelihood functions proposed by Royall and Tsou (2003). Next, we provide a parametric robust method originated from this idea to make inferences for correlated ordinal data and develop the robust likelihood functions for regression coefficients of mean modeled in a generalized linear model fashion. Finally, we extend the robust likelihood technique from generalized linear models (GLM) to partially-linear models (PLM), and use normal distribution as the working model to develop the robust likelihood functions for regression coefficients in large samples. The legitimacy of this novel approach requires no knowledge of the underlying joint distributions so long as their second or fourth moments exist. The efficacy of the proposed parametric approach is demonstrated via simulations and the analyses of several real data sets.