A Consistent Estimation in Log-Linear Mixed Measurement Error Models

• Main Authors: Jui-Chun Chang, 張瑞君
• Other Authors: 黃逸輝
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/85445382018405340454

碩士 === 淡江大學 === 數學學系碩士班 === 97 === By introducing the random effect into a regression model, the correlation between responses raises consequently. Thus when a model includes random effects, which is called a mixed model, can be suitable for modeling correlated responses. In most mixed models, the likelihood-based inferences are usually applicable. However, when any covariate in the regression model are subject to measurement error, the statistical inference becomes difficult for the reason that the likelihood approach is not feasible in most measurement error problems, especially for the functional cases. For this difficulty, there are only few literatures dealing with the mixed model with measurement error nowadays. Furthermore, to the best knowledge of the author, there is no consistent estimation for the log-linear mixed effect model when measurement error presents. In this thesis, inspired by the quasi-variance function and the corrected score, we construct estimating function for log-linear mixed model with classical additive measurement error. It is shown that the estimation is consistent and our simulation study indicates that the proposed estimating function works satisfactory.