Robust Regression Estimators in Gene Expression Analysis

• Main Authors: Chang, Yu-Hua, 張祐華
• Other Authors: Chen, Lin-An
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/36026758076067900469

碩士 === 國立交通大學 === 統計學研究所 === 101 === Discovering the influential genes through the detection of outliers in samples from disease group subjects is a very new and important approach for gene expression analysis Technique of outlier least squares estimator for regression model has been found in literature that unfortunately its influence function can not limit the eect of independent variables We present asymptotic distributions of the mallows type bounded influence outlier least squares estimator and outlier regression quantile for linear regression models producing statistical techniques with influence functions bounded in the space of independent variables Monte Carlo simulations comparing mean squared errors show that the bounded influence ones are more effcient than the unbounded influence ones when gross errors occur in the independent variable space