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A semiparametric regression model for longitudinal data with non-stationary errors

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Li, Rui, Leng, Chenlei and You, Jinhong (2017) A semiparametric regression model for longitudinal data with non-stationary errors. Scandinavian Journal of Statistics, 44 (4). pp. 932-950. doi:10.1111/sjos.12284

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Official URL: http://dx.doi.org/10.1111/sjos.12284

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Abstract

Motivated by the need to analyze the National Longitudinal Surveys data, we propose a new semiparametric longitudinal mean-covariance model in which the effects on dependent variable of some explanatory variables are linear and others are non-linear, while the within-subject correlations are modelled by a non-stationary autoregressive error structure. We develop an estimation machinery based on least squares technique by approximating non-parametric functions via B-spline expansions and establish the asymptotic normality of parametric estimators as well as the rate of convergence for the non-parametric estimators. We further advocate a new model selection strategy in the varying-coefficient model framework, for distinguishing whether a component is significant and subsequently whether it is linear or non-linear. Besides, the proposed method can also be employed for identifying the true order of lagged terms consistently. Monte Carlo studies are conducted to examine the finite sample performance of our approach, and an application of real data is also illustrated.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Regression analysis, Longitudinal method, Autoregression (Statistics), Least squares, Nonparametric statistics, Convergence, Monte Carlo method
Journal or Publication Title: Scandinavian Journal of Statistics
Publisher: Wiley-Blackwell Publishing Ltd.
ISSN: 0303-6898
Official Date: December 2017
Dates:
DateEvent
December 2017Published
6 June 2017Available
12 March 2017Accepted
Volume: 44
Number: 4
Page Range: pp. 932-950
DOI: 10.1111/sjos.12284
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
11471203[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
2016LZ22National Statistical Science Research ProjectUNSPECIFIED
UNSPECIFIEDShanghai Universityhttp://dx.doi.org/10.13039/501100009002
16PJC042Shanghai Pujiang ProgramUNSPECIFIED
IRTSHUFEProgram for Innovative Research Team of Shanghai University of Finance and EconomicsUNSPECIFIED
IRT13077rogram for Changjiang Scholars and Innovative Research Team in UniversityUNSPECIFIED

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