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Activity Number: 410 - High-Dimensional Regression
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #328937 Presentation
Title: Simulation-Selection-Extrapolation Estimator for High-Dimensional Errors-In-Variables Models
Author(s): Linh Nghiem* and Cornelis Potgieter
Companies: Southern Methodist University and Southern Methodist University
Keywords: high dimensional; measurement error; simex; logistic regression; Cox survival

This paper considers errors-in-variables models in a high-dimensional setting. When no measurement error is present in the covariates, the lasso is often used for estimation in high-dimensional models; however, the presence of measurement error can result in severely biased parameter estimates and also affects the ability of the lasso to recover the true sparsity pattern. A new estimator, called SIMulation-SELection-EXtrapolation (SIMSELEX) is proposed. Central to the new estimator is the application of simulation-extrapolation procedure (SIMEX) to the lasso in combination with a variable selection step after the simulation step but before the extrapolation step. The SIMSELEX estimator is shown to perform well in variable selection and has a significantly lower estimation error than the naive estimators that ignores the measurement errors. Furthermore, SIMSELEX can be applied in every errors-in-variables setting; this paper illustrates this paper illustrates applications in linear regression, logistic regression, Cox survival model, and nonparametric model. The method is used to analyze a dataset that contains gene expression measurements of favorable histology Wilms tumors.

Authors who are presenting talks have a * after their name.

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