Simultaneous variable selection and estimation in semiparametric modeling of longitudinal/clustered data

By Shujie Ma, Qiongxia Song, Li Wang. 2013

57 citations recorded by the source corpus.

We consider the problem of simultaneous variable selection and estimation in additive, partially linear models for longitudinal/clustered data. We propose an estimation procedure via polynomial splines to estimate the nonparametric components and apply proper penalty functions to achieve sparsity in the linear part. Under reasonable conditions, we obtain the asymptotic normality of the estimators for the linear components and the consistency of the estimators for the nonparametric components. We further demonstrate that, with proper choice of the regularization parameter, the penalized estimators of the non-zero coefficients achieve the asymptotic oracle property. The finite sample behavior of the penalized estimators is evaluated with simulation studies and illustrated by a longitudinal CD4 cell count data set.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/16d1e25451998f44aeda2c88c3a9a824554892c2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shujie Ma
2. Peter X.-K. Song
3. Hua Liang
4. Annie Qu
5. Li Wang
6. Lan Xue
7. Lijian Yang
8. Raymond K. Wong

In Statistics and Probability

1. Hua Liang
2. Li Wang
3. Shujie Ma
4. Peter X.-K. Song
5. Lan Xue
6. Annie Qu
7. Xinyu Zhang
8. Lijian Yang

Citations in the corpus

• Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates (2014, methodology): “In addition, the required techniques using the penalized quadratic distance function for the diverging numbers of linear and nonlinear covariates setting are very different from existing approaches such as the penalized least-squares approach for a finite dimension setting [20, 25, 31]; the generali”
• Varying Index Coefficient Models (2015, methodology): “On the other hand, the spline estimation approach is also known to be computationally faster than kernel smoothing in semiparametric models (Ma, Song, and Wang 2013; Wang and Yang 2009a; Wang et al. 2011).”