Semiparametric partially linear regression models for functional data

In the context of longitudinal data analysis, a random function typically represents a subject that is often observed at a small number of time point. For discarding this restricted condition of observation number of each subject, we consider the semiparametric partially linear regression models wit...

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Published inJournal of statistical planning and inference Vol. 142; no. 9; pp. 2518 - 2529
Main Authors Zhang, Tao, Wang, Qihua
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.09.2012
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ISSN0378-3758
1873-1171
DOI10.1016/j.jspi.2012.03.004

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Abstract In the context of longitudinal data analysis, a random function typically represents a subject that is often observed at a small number of time point. For discarding this restricted condition of observation number of each subject, we consider the semiparametric partially linear regression models with mean function x⊤β + g(z), where x and z are functional data. The estimations of β and g(z) are presented and some asymptotic results are given. It is shown that the estimator of the parametric component is asymptotically normal. The convergence rate of the estimator of the nonparametric component is also obtained. Here, the observation number of each subject is completely flexible. Some simulation study is conducted to investigate the finite sample performance of the proposed estimators.
AbstractList In the context of longitudinal data analysis, a random function typically represents a subject that is often observed at a small number of time point. For discarding this restricted condition of observation number of each subject, we consider the semiparametric partially linear regression models with mean function x⊤β + g(z), where x and z are functional data. The estimations of β and g(z) are presented and some asymptotic results are given. It is shown that the estimator of the parametric component is asymptotically normal. The convergence rate of the estimator of the nonparametric component is also obtained. Here, the observation number of each subject is completely flexible. Some simulation study is conducted to investigate the finite sample performance of the proposed estimators.
Author Wang, Qihua
Zhang, Tao
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Keywords Semiparametric partially linear regression models
Longitudinal data
Functional data
Asymptotic normality
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Snippet In the context of longitudinal data analysis, a random function typically represents a subject that is often observed at a small number of time point. For...
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SubjectTerms Asymptotic normality
Functional data
Longitudinal data
Semiparametric partially linear regression models
Title Semiparametric partially linear regression models for functional data
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