 Estimation of semi-varying coefficient models for longitudinal data with irregular error structureYan-Yong Zhao, Jin-Guan Lin, Jian-Qiang Zhao and Zhang-Xiao Miao Computational Statistics & Data Analysis, 2022, vol. 169, issue C Abstract: Semiparametric models are often considered for modeling longitudinal data for a good balance between flexibility and parsimony. In this paper, we focus on the estimation for a longitudinal semi-varying coefficient model which is of irregular errors. A semiparametric profile least-squares method is developed to estimate parameters in the mean function and error structure simultaneously. Then, a two-stage local linear estimator is investigated for the nonparametric part. Further, we establish the asymptotic properties of the resulting estimators under some mild conditions. The practical problems of implementation are also addressed. Finally, three numerical experiments are conducted to verify the finite sample performance of the proposed methods, and an application to the CD4 cell data is provided for illustration. Keywords: Local linear estimation; Irregular error structure; Semiparametric profile least-squares; Two-stage local linear estimator (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:169:y:2022:i:c:s0167947321002231 DOI: 10.1016/j.csda.2021.107389 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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