期刊
BIOMETRIKA
卷 89, 期 1, 页码 111-128出版社
BIOMETRIKA TRUST
DOI: 10.1093/biomet/89.1.111
关键词
basis function; confidence band; hypothesis testing; least squares; longitudinal data; polynomial spline; resampling subject bootstrap; varying-coefficient model
A global smoothing procedure is developed using basis function approximations for estimating the parameters of a varying-coefficient model with repeated measurements. Inference procedures based on a resampling subject bootstrap are proposed to construct confidence regions and to perform hypothesis testing. Conditional biases and variances of our estimators and their asymptotic consistency are developed explicitly. Finite sample properties of our procedures are investigated through a simulation study. Application of the proposed approach is demonstrated through an example in epidemiology. In contrast to the existing methods, this approach applies whether or not the covariates are time-invariant and does not require binning of the data when observations are sparse at distinct observation times.
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