Generalized additive partial linear models for clustered data with diverging number of covariates using gee
We study flexible modeling of clustered data using marginal generalized additive partial linear models with a diverging number of covariates. Generalized estimating equations are used to fit the model with the nonparametric functions being approximated by polynomial splines. We investigate the asymp...
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sg-ntu-dr.10356-1051202019-12-06T21:46:15Z Generalized additive partial linear models for clustered data with diverging number of covariates using gee Wang, Lan Lian, Heng Liang, Hua School of Physical and Mathematical Sciences DRNTU::Science::Mathematics::Statistics We study flexible modeling of clustered data using marginal generalized additive partial linear models with a diverging number of covariates. Generalized estimating equations are used to fit the model with the nonparametric functions being approximated by polynomial splines. We investigate the asymptotic properties in a "large n, diverging p" framework. More specifically, we establish the consistency and asymptotic normality of the estimators for the linear parameters under mild conditions. We propose a penalized estimating equations based procedure for simultaneous variable selection and estimation. The proposed variable selection procedure enjoys the oracle property and allows the number of parameters in the linear part to increase at the same order as the sample size under some general conditions. Extensive Monte Carlo simulations demonstrate that the proposed methods work well with moderate sample sizes. a dataset is analyzed to illustrate the application. 2014-09-01T06:05:58Z 2019-12-06T21:46:15Z 2014-09-01T06:05:58Z 2019-12-06T21:46:15Z 2014 2014 Journal Article Lian, H., Liang, H., & Wang, L. (2014). Generalized additive partial linear models for clustered data with diverging number of covariates using gee. Statistica Sinica, 24, 173-196. https://hdl.handle.net/10356/105120 http://hdl.handle.net/10220/20452 http://www3.stat.sinica.edu.tw/statistica/j24n1/j24n19/j24n19.html en Statistica sinica © 2014 Statistica Sinica. |
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DRNTU::Science::Mathematics::Statistics Wang, Lan Lian, Heng Liang, Hua Generalized additive partial linear models for clustered data with diverging number of covariates using gee |
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We study flexible modeling of clustered data using marginal generalized additive partial linear models with a diverging number of covariates. Generalized estimating equations are used to fit the model with the nonparametric functions being approximated by polynomial splines. We investigate the asymptotic properties in a "large n, diverging p" framework. More specifically, we establish the consistency and asymptotic normality of the estimators for the linear parameters under mild conditions. We propose a penalized estimating equations based procedure for simultaneous variable selection and estimation. The proposed variable selection procedure enjoys the oracle property and allows the number of parameters in the linear part to increase at the same order as the sample size under some general conditions. Extensive Monte Carlo simulations demonstrate that the proposed methods work well with moderate sample sizes. a dataset is analyzed to illustrate the application. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Wang, Lan Lian, Heng Liang, Hua |
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Article |
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Wang, Lan Lian, Heng Liang, Hua |
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Wang, Lan |
title |
Generalized additive partial linear models for clustered data with diverging number of covariates using gee |
title_short |
Generalized additive partial linear models for clustered data with diverging number of covariates using gee |
title_full |
Generalized additive partial linear models for clustered data with diverging number of covariates using gee |
title_fullStr |
Generalized additive partial linear models for clustered data with diverging number of covariates using gee |
title_full_unstemmed |
Generalized additive partial linear models for clustered data with diverging number of covariates using gee |
title_sort |
generalized additive partial linear models for clustered data with diverging number of covariates using gee |
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2014 |
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https://hdl.handle.net/10356/105120 http://hdl.handle.net/10220/20452 http://www3.stat.sinica.edu.tw/statistica/j24n1/j24n19/j24n19.html |
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