کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1144699 | 957428 | 2014 | 12 صفحه PDF | دانلود رایگان |
This paper considers robust variable selection in semiparametric modeling for longitudinal data with an unspecified dependence structure. First, by basis spline approximation and using a general formulation to treat mean, median, quantile and robust mean regressions in one setting, we propose a weighted M-type regression estimator, which achieves robustness against outliers in both the response and covariates directions, and can accommodate heterogeneity, and the asymptotic properties are also established. Furthermore, a penalized weighted M-type estimator is proposed, which can do estimation and select relevant nonparametric and parametric components simultaneously, and robustly. Without any specification of error distribution and intra-subject dependence structure, the variable selection method works beautifully, including consistency in variable selection and oracle property in estimation. Simulation studies also confirm our method and theories.
Journal: Journal of the Korean Statistical Society - Volume 43, Issue 2, June 2014, Pages 303–314