Estimation and model identification of longitudinal data time-varying nonparametric models
In this paper, we consider nonparametric regression modeling for longitudinal data. An important modeling choice is that the covariate effect may change dynamically with time by using a bivariate link function. Comparing with Jiang and Wang (2010, 2011), and Zhang et al. (2013) we make two distinct...
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| Published in | Journal of multivariate analysis Vol. 156; pp. 116 - 136 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Elsevier Inc
01.04.2017
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| Online Access | Get full text |
| ISSN | 0047-259X 1095-7243 |
| DOI | 10.1016/j.jmva.2017.02.003 |
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| Abstract | In this paper, we consider nonparametric regression modeling for longitudinal data. An important modeling choice is that the covariate effect may change dynamically with time by using a bivariate link function. Comparing with Jiang and Wang (2010, 2011), and Zhang et al. (2013) we make two distinct contributions to this important class of models. First, we show theoretically and empirically that taking the within-subject correlation into account can improve the estimation efficiency for the bivariate link function. Second, we propose a novel method involving a shrinkage estimation technique to identify consistently whether the effect of covariates is time-varying. Simulation studies are conducted to assess the finite-sample performance and a real data example is analyzed to illustrate the proposed methods. |
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| AbstractList | In this paper, we consider nonparametric regression modeling for longitudinal data. An important modeling choice is that the covariate effect may change dynamically with time by using a bivariate link function. Comparing with Jiang and Wang (2010, 2011), and Zhang et al. (2013) we make two distinct contributions to this important class of models. First, we show theoretically and empirically that taking the within-subject correlation into account can improve the estimation efficiency for the bivariate link function. Second, we propose a novel method involving a shrinkage estimation technique to identify consistently whether the effect of covariates is time-varying. Simulation studies are conducted to assess the finite-sample performance and a real data example is analyzed to illustrate the proposed methods. |
| Author | Lian, Heng You, Jinhong Liu, Shu |
| Author_xml | – sequence: 1 givenname: Shu surname: Liu fullname: Liu, Shu email: liu2008shu@126.com organization: School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, PR China – sequence: 2 givenname: Jinhong surname: You fullname: You, Jinhong organization: School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, PR China – sequence: 3 givenname: Heng surname: Lian fullname: Lian, Heng organization: Department of Mathematics, City University of Hong Kong, Kowloon Tong, HK, 999077, Hong Kong |
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