Estimation and model identification of longitudinal data time-varying nonparametric models
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 116-136 |
Journal / Publication | Journal of Multivariate Analysis |
Volume | 156 |
Publication status | Published - 1 Apr 2017 |
Link(s)
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.
Research Area(s)
- Longitudinal data, Model identification, Modified Cholesky decomposition, Nonparametric regression, Time-varying
Citation Format(s)
Estimation and model identification of longitudinal data time-varying nonparametric models. / Liu, Shu; You, Jinhong; Lian, Heng.
In: Journal of Multivariate Analysis, Vol. 156, 01.04.2017, p. 116-136.
In: Journal of Multivariate Analysis, Vol. 156, 01.04.2017, p. 116-136.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review