Variable Selection for Partially Linear Models With Measurement Errors
نویسندگان
چکیده
منابع مشابه
Variable Selection for Partially Linear Models with Measurement Errors.
This article focuses on variable selection for partially linear models when the covariates are measured with additive errors. We propose two classes of variable selection procedures, penalized least squares and penalized quantile regression, using the nonconvex penalized principle. The first procedure corrects the bias in the loss function caused by the measurement error by applying the so-call...
متن کاملGeneralized varying coefficient partially linear measurement errors models
We study generalized varying coefficient partially linearmodels when some linear covariates are error prone, but their ancillary variables are available. We first calibrate the error-prone covariates, then develop a quasi-likelihood-based estimation procedure. To select significant variables in the parametric part, we develop a penalized quasi-likelihood variable selection procedure, and the re...
متن کاملAutomatic model selection for partially linear models
We propose and study a unified procedure for variable selection in partially linear models. A new type of double-penalized least squares is formulated, using the smoothing spline to estimate the nonparametric part and applying a shrinkage penalty on parametric components to achieve model parsimony. Theoretically we show that, with proper choices of the smoothing and regularization parameters, t...
متن کاملVariable Selection for Partially Linear Varying Coefficient Transformation Models with Censored Data
In this paper, we study the problem of variable selection for varying coefficient transformation models with censored data. We fit the varying coefficient transformation models by maximizing the marginal likelihood subject to a shrinkage-type penalty, which encourages sparse solutions and hence facilitates the process of variable selection. We further provide an efficient computation algorithm ...
متن کاملVariable selection in linear models
Variable selection in linear models is essential for improved inference and interpretation, an activity which has become even more critical for high dimensional data. In this article, we provide a selective review of some classical methods including Akaike information criterion, Bayesian information criterion, Mallow’s Cp and risk inflation criterion, as well as regularization methods including...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2009
ISSN: 0162-1459,1537-274X
DOI: 10.1198/jasa.2009.0127