A Semiparametric Regression Model for Longitudinal Data with Non-stationary Errors

    2017年，我校统计与信息学院李睿在国际一类杂志《Scandinavian Journal of Statistics》发表“A Semiparametric Regression Model for Longitudinal Data with Non-stationary Errors”论文。

作者:

Li, Rui¹
Leng, Chenlei²
You, Jinhong^3,4

来源:

Scandinavian Journal of Statistics. Dec2017, Vol. 44 Issue 4, p932-950. 19p.

文献类型:

Article

主题语:

*Parameter estimation
*Regression analysis
*Error analysis (Mathematics)
*Analysis of covariance
Longitudinal method

作者提供的关键字:

autoregressive process
B-splines
model selection
rate of convergence
SCAD penalty

摘要:

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. [ABSTRACT FROM AUTHOR]

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作者单位:

¹School of Statistics and Information, Shanghai University of International Business and Economics
²Department of Statistics, University of Warwick
³Key Laboratory of Mathematical Economics (SUFE), Ministry of Education of China
⁴School of Statistics and Management, Shanghai University of Finance and Economics

ISSN:

0303-6898

DOI:

10.1111/sjos.12284

入藏编号:

126089814

出版者徽标: