Two step estimations for a single-index varying-coefficient model with longitudinal data
type: Item
instance of: scholarly article
title: Two step estimations for a single-index varying-coefficient model with longitudinal data
zbMATH Open document ID: 1408.62070
author: Chaohui Guo
author: Hu Yang
author: Jing Lv
published in: Statistical Papers
publication date: 2018-10-01T00:00:00Z
review text: In this paper, a two-step estimation procedure is proposed to improve the estimation efficiency of the index coefficients in a single-index varying-coefficient model with longitudinal data. In the first step, initial estimators are produced by ignoring the possible correlations between repeated measures. Based on the modified Cholesky decomposition, the least squares technique is applied for estimating the autoregressive coefficients and the innovation variance, and then obtaining the estimated within-subject covariance matrix. In the second step, centralized generalized estimating equations are built to generate more efficient estimators of the index coefficients. Based on the estimated index coefficients, estimators of the unknown functions are obtained by employing a weighted least squares approach. Simulation studies and an analysis of real progesterone data are presented to demonstrate the efficiency of the estimators.
reviewed by: Annibal Parracho Sant'Anna
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62G05
Mathematics Subject Classification ID: 62G20
zbMATH DE Number: 6945910
zbMATH Keywords: efficiency
zbMATH Keywords: generalized estimating equations
zbMATH Keywords: local linear regression
zbMATH Keywords: longitudinal data
zbMATH Keywords: modified Cholesky decomposition
zbMATH Keywords: single-index varying-coefficient model
zbMATH Keywords: correlations between repeated measures
MaRDI profile type: Publication
full work available at URL: s00362-016-0798-z
OpenAlex ID: W2471512844
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DOI: 10.1007/S00362-016-0798-Z
description: scientific article; zbMATH DE number 6945910