Variable selection and estimation for partially linear single-index models with longitudinal data
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 579-593 |
Journal / Publication | Statistics and Computing |
Volume | 25 |
Issue number | 3 |
Online published | 25 Feb 2014 |
Publication status | Published - May 2015 |
Externally published | Yes |
Link(s)
Abstract
In this paper, we consider the partially linear single-index models with longitudinal data. To deal with the variable selection problem in this context, we propose a penalized procedure combined with two bias correction methods, resulting in the bias-corrected generalized estimating equation and the bias-corrected quadratic inference function, which can take into account the correlations. Asymptotic properties of these methods are demonstrated. We also evaluate the finite sample performance of the proposed methods via Monte Carlo simulation studies and a real data analysis.
Research Area(s)
- Bias correction, Longitudinal data, Partially linear single-index model, Variable selection
Citation Format(s)
Variable selection and estimation for partially linear single-index models with longitudinal data. / Li, Gaorong; Lai, Peng; Lian, Heng.
In: Statistics and Computing, Vol. 25, No. 3, 05.2015, p. 579-593.
In: Statistics and Computing, Vol. 25, No. 3, 05.2015, p. 579-593.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review