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Full-Text Articles in Physical Sciences and Mathematics
Categorizing A Continuous Predictor Subject To Measurement Error, Betsabé G. Blas Achic, Tianying Wang, Ya Su, Victor Kipnis, Kevin Dodd, Raymond J. Carroll
Categorizing A Continuous Predictor Subject To Measurement Error, Betsabé G. Blas Achic, Tianying Wang, Ya Su, Victor Kipnis, Kevin Dodd, Raymond J. Carroll
Statistics Faculty Publications
Epidemiologists often categorize a continuous risk predictor, even when the true risk model is not a categorical one. Nonetheless, such categorization is thought to be more robust and interpretable, and thus their goal is to fit the categorical model and interpret the categorical parameters. We address the question: with measurement error and categorization, how can we do what epidemiologists want, namely to estimate the parameters of the categorical model that would have been estimated if the true predictor was observed? We develop a general methodology for such an analysis, and illustrate it in linear and logistic regression. Simulation studies are …
Mixtures-Of-Regressions With Measurement Error, Xiaoqiong Fang
Mixtures-Of-Regressions With Measurement Error, Xiaoqiong Fang
Theses and Dissertations--Statistics
Finite Mixture model has been studied for a long time, however, traditional methods assume that the variables are measured without error. Mixtures-of-regression model with measurement error imposes challenges to the statisticians, since both the mixture structure and the existence of measurement error can lead to inconsistent estimate for the regression coefficients. In order to solve the inconsistency, We propose series of methods to estimate the mixture likelihood of the mixtures-of-regressions model when there is measurement error, both in the responses and predictors. Different estimators of the parameters are derived and compared with respect to their relative efficiencies. The simulation results …