Comparisons Between Local Linear Estimator and Kernel Smooth Estimator for a Smooth Distribution Based on MSE Under Right Censoring

We describe a method for estimating the coefficients in a logistic regression model when the predictors are subject to measurement error and an instrumental variable is present. The proposed method is based upon the theory of factor scores taken from factor analysis. Two versions of the proposed method, a simple one and an extended one, are compared to the methods referred to by Carrol, Ruppert and Stefanski (1995) through simulation studies. Our conclusion is that the simple version performs as well as the methods from Carrol et al. (1995), and the extended version performs betterwith respect to MSE, due to a reduction of bias.