A0287
Title: An integrated square distance method for logistic errors-in-variables regression under case control studies
Authors: Pei Geng - University of New Hampshire (United States) [presenting]
Abstract: In logistic regression under the case-control framework, the logarithmic ratio of the covariate densities between the case and control groups is a linear function of the regression parameters. An integrated squared distance (ISD) can be used to obtain the parameter estimators based on the estimated covariate densities. When measurement error is present in the covariate variables, the deconvolution kernel density estimation is adopted in the ISD approach, assuming the error density is known. When validation data is available, a semiparametric method is proposed in the ISD approach to reduce the estimation bias. These methods are demonstrated by both simulation study and real data application.
