Adaptive LASSO for varying-coefficient partially linear measurement error models

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)40-54
Journal / PublicationJournal of Statistical Planning and Inference
Issue number1
Publication statusPublished - Jan 2013


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. © 2012 Elsevier B.V.

Research Area(s)

  • Adaptive LASSO, LARS, Measurement errors, Model selection, Oracle property, SCAD, Semi-parametric model