Journal ArticleDOI
Inferences for a semiparametric model with panel data
SC Cheng,Lee-Jen Wei +1 more
TLDR
In this article, a semiparametric model which relates the mean of the response variable at each time point proportionally to a function of a time-dependent covariate vector is proposed.Abstract:
SUMMARY In a longitudinal study, suppose that, for each subject, repeated measurements of the response variable and covariates are collected at a set of distinct, irregularly spaced time points. We consider a semiparametric model which relates the mean of the response variable at each time point proportionally to a function of a time-dependent covariate vector to analyse such panel data. Inference procedures for regression parameters are proposed without involving any nonparametric function estimation for the nuisance mean function. A dataset from a recent AIDS clinical trial is used to illustate the new proposal.read more
Citations
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Journal ArticleDOI
New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis
Jianqing Fan,Runze Li +1 more
TL;DR: In this paper, two new approaches are proposed for estimating the regression coefficients in a semiparametric model and the asymptotic normality of the resulting estimators is established.
Journal ArticleDOI
Semiparametric and Nonparametric Regression Analysis of Longitudinal Data
Danyu Lin,Zhiliang Ying +1 more
TL;DR: In this paper, the marginal distribution for the response variable Y at time t is related to the vector of possibly time-varying covariates X through the equations E{Y(t)|| X(t} = α0(t) + β′0X(t), where α0 (t) is an arbitrary function of t, β 0 is a vector of constant regression coefficients, and β 0(t)-x is a variable of time varying regression coefficients.
Journal ArticleDOI
Smoothing Spline Estimation for Varying Coefficient Models With Repeatedly Measured Dependent Variables
TL;DR: In this paper, a componentwise smoothing spline method was proposed for estimating β 0(t, ε(t), ε, β k(t) nonparametrically based on the previous varying coefficient model and a longitudinal sample of (t,Y,t,X) with time-independent covariates.
Journal ArticleDOI
Empirical Likelihood Semiparametric Regression Analysis for Longitudinal Data
Liugen Xue,Lixing Zhu +1 more
TL;DR: In this article, a groupwise empirical likelihood procedure was proposed to handle the inter-series dependence for the longitudinal semiparametric regression model, and employed bias correction to construct the empirical likelihood ratio functions for the parameters of interest.
Journal ArticleDOI
Semiparametric analysis of panel count data with correlated observation and follow-up times.
Xin He,Xingwei Tong,Jianguo Sun +2 more
TL;DR: In this article, the authors discuss regression analysis of panel count data that often arise in longitudinal studies concerning occurrence rates of certain recurrent events and propose some shared frailty models and estimating equations are developed for estimation of regression parameters.
References
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Journal ArticleDOI
Longitudinal data analysis using generalized linear models
Kung Yee Liang,Scott L. Zeger +1 more
TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.
Journal ArticleDOI
Random-effects models for longitudinal data
Nan M. Laird,James H. Ware +1 more
TL;DR: In this article, a unified approach to fitting two-stage random-effects models, based on a combination of empirical Bayes and maximum likelihood estimation of model parameters and using the EM algorithm, is discussed.
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Analysis of longitudinal data
TL;DR: In this paper, a generalized linear model for longitudinal data and transition models for categorical data are presented. But the model is not suitable for categric data and time dependent covariates are not considered.
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Approximation Theorems of Mathematical Statistics
TL;DR: In this paper, the basic sample statistics are used for Parametric Inference, and the Asymptotic Theory in Parametric Induction (ATIP) is used to estimate the relative efficiency of given statistics.
Journal ArticleDOI
Cox's Regression Model for Counting Processes: A Large Sample Study
TL;DR: In this article, the Cox regression model for censored survival data is extended to a model where covariate processes have a proportional effect on the intensity process of a multivariate counting process, allowing for complicated censoring patterns and time dependent covariates.
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