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Kernel regression estimation using repeated measurements data. (English) Zbl 0635.62030
The nonparametric estimation of an average growth curve has been considered. It is supposed that there are observations from several experimental units, each following the regression model \(y(x_ j)=f(x_ j)+\epsilon_ j\) \((j=1,...,n)\), where \(\epsilon_ 1,...,\epsilon_ n\) are correlated zero mean errors and \(0\leq x_ 1<...<x_ n\leq 1\) are fixed constants.
Asymptotic and finite-sample results concerning the mean squared error of the estimator are obtained. The influence of correlation on the bandwidth minimizing mean squared error is discussed. A data-based method for selecting the bandwidth is illustrated in a data analysis.
Reviewer: V.P.Gupta

MSC:
62G05 Nonparametric estimation
62G99 Nonparametric inference
62J99 Linear inference, regression
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