On quadratic logistic regression models when predictor variables are subject to measurement error
學年 104
學期 2
出版（發表）日期 2016-03-01
作品名稱 On quadratic logistic regression models when predictor variables are subject to measurement error
作品名稱（其他語言）
著者 Jakub Stoklosa; Yih-Huei Huang; Elise Furlan; Wen-Han Hwang
單位
出版者
著錄名稱、卷期、頁數 Computational Statistics and Data Analysis 95, p.109–121
摘要 Owing to its good properties and a simple model fitting procedure, logistic regression is one of the most commonly used methods applied to data consisting of binary outcomes and one or more predictor variables. However, if the predictor variables are measured with error and the functional relationship between the response and predictor variables is non-linear (e.g., quadratic) then consistent estimation of model parameters is more challenging to develop. To address the effects of measurement error in predictor variables when using quadratic logistic regression models, two novel approaches are developed: (1) an approximated refined regression calibration; and (2) a weighted corrected score method. Both proposed approaches offer several advantages over existing methods in that they are computationally efficient and are straightforward to implement. A simulation study was conducted to evaluate the estimators’ finite sample performance. The proposed methods are also applied on real data from a medical study and an ecological application.
關鍵字 Functional measurement error;Quadratic logistic regression;Regression calibration;Weighted corrected score
語言 en
ISSN 0167-9473
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Wen-Han Hwang
審稿制度 否
國別 USA
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/105923 )