Semiparametric Estimation in Logistic Measurement Error Models
Author | : Raymond J. Carroll |
Publisher | : |
Total Pages | : 30 |
Release | : 1989 |
ISBN-10 | : OCLC:1030896154 |
ISBN-13 | : |
Rating | : 4/5 (54 Downloads) |
Book excerpt: We describe semiparametric estimation and inference in a logistic regression model with measurement error in the predictors. The particular measurement error model consists of a primary data set in which only the response Y and a fallible surrogate W of the true predictor X are observed, plus a smaller validation data set for which (Y, X, W) are observed. Except for the underlying assumption of a logistic model in the true predictor, no parametric distributional assumptions are made about the true predictor or its surrogate. We develop a semiparametric parameter estimate of the logistic regression parameter which is asymptotically normally distributed and computationally feasible. The estimate relies on kernel regression techniques. For scalar predictors, by a detailed analysis of the mean-squared error of the parameter estimate, we obtain a representation for an optimal bandwidth.