A semiparametric additive regression model for longitudinal data

In previous work we have studied a nonparametric additive time-varying regression model for longitudinal data recorded at irregular intervals. The model allows the influence of each covariate to vary separately with time. For small datasets, however, only a limited number of covariates may be handle...

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Bibliographic Details
Published in:Biometrika 1999-09, Vol.86 (3), p.691-702
Main Authors: Martinussen, T, Scheike, TH
Format: Article
Language:English
Subjects:
Dynamic linear model
Estimators
Exact sciences and technology
Least squares
Linear inference, regression
Liver diseases
Local estimating equation
Longitudinal data
Marked point process
Martingale
Martingales
Mathematics
Modeling
Nonparametric models
Parametric models
Point estimators
Probability and statistics
Regression analysis
Sciences and techniques of general use
Statistics
Survival analysis
Time-varying coefficients
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Summary:In previous work we have studied a nonparametric additive time-varying regression model for longitudinal data recorded at irregular intervals. The model allows the influence of each covariate to vary separately with time. For small datasets, however, only a limited number of covariates may be handled in this way. In this paper, we introduced a semiparametric regression model for longitudinal data. The influence of some of the covariates varies nonparametrically with time while the effect of the remaining covariates are constant. No smoothing is necessary in the estimation of the parametric terms of the model. Asymptotics are derived using martingale techniques for the cumulative regression functions, which are much easier to estimate and study than the regression functions themselves. The approach is applied to longitudinal data from the Copenhagen Study Group for Liver Diseases (Schlichting et al., 1983).
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/86.3.691