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

文章来源:科研处 编辑: 发布时间:2018-03-27 浏览次数:242


    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。



编辑:
Li, Rui1
Leng, Chenlei2
You, Jinhong3,4
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
编辑提供的关键字:
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]
Copyright of Scandinavian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.(Copyright applies to all Abstracts.)
编辑单位:
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:
出版者徽标



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