Adaptive LASSO for varying-coefficient partially linear measurement error models |
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Authors: | HaiYing Wang Guohua Zou Alan TK Wan |
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Institution: | 1. Department of Statistics, University of Missouri, Columbia, MO 65211, USA;2. MADIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China;3. Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong |
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Abstract: | This paper extends the adaptive LASSO (ALASSO) for simultaneous parameter estimation and variable selection to a varying-coefficient partially linear model where some of the covariates are subject to measurement errors of an additive form. We draw comparisons with the SCAD, and prove that both the ALASSO and the SCAD attain the oracle property under this setup. We further develop an algorithm in the spirit of LARS for finding the solution path of the ALASSO in practical applications. Finite sample properties of the proposed methods are examined in a simulation study, and a real data example based on the U.S. Department of Agriculture's Continuing Survey of Food Intakes by Individuals (CSFII) is considered. |
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Keywords: | Adaptive LASSO LARS Measurement errors Model selection Oracle property SCAD Semi-parametric model |
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