On quadratic logistic regression models when predictor variables are subject to measurement error

doi:10.1016/j.csda.2015.09.012

淡江大學機構典藏 > 理學院 > 數學學系暨研究所 > 期刊論文 > Item 987654321/105923

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題名:	On quadratic logistic regression models when predictor variables are subject to measurement error
作者:	Jakub Stoklosa;Yih-Huei Huang;Elise Furlan;Wen-Han Hwang
關鍵詞:	Functional measurement error;Quadratic logistic regression;Regression calibration;Weighted corrected score
日期:	2015-10-31
上傳時間:	2016-04-22 13:11:09 (UTC+8)
出版者:	ELSEVIER
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.
關聯:	Computational Statistics and Data Analysis 95, p.109–121
DOI:	10.1016/j.csda.2015.09.012
顯示於類別:	[數學學系暨研究所] 期刊論文

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