淡江大學 數學系 資統組 演講

主 講 人： 王清雲      博士 (Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, P.O. Box 19024, Seattle, WA 98109-1024, USA.)
講   題： Robust Best Linear Estimation for Poisson Regression with Heterosedastic Measurement Error Using Instrumental Variables in Calibration Sample
日    期：99年2月23日（星期二）
時    間：下午2:30 –3:20
地    點：數學系(科學館S436室)
摘    要：
We investigate methods for Poisson regression when covariates are measured with errors, and the measurement error may be heteroscedastic. In the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies the classical measurement error model, but it may not have repeated measurements and the variance of the measurement error may not be a constant. We assume that an additional instrumental variable is available for subjects in a subset, namely a calibration sample, such that the instrumental variable is a function of the unobserved exposure variable. The errors of the instrumental variable may also be heteroscedastic. We propose a robust best linear estimator that uses all the available data. The proposed estimator is shown to be consistent and asymptotically normal under very weak distributional assumptions. Finite sample performance of the proposed estimator is examined and compared with many instrumental variable estimators via intensive simulation studies.