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\iteman{ZMATH 1255.62115}
\itemau{Manteiga, Wenceslao Gonz\'alez; Lombard{\'\i}a, Mar{\'\i}a Jos\'e; Miranda, Mar{\'\i}a Dolores Mart{\'\i}nez; Sperlich, Stefan}
\itemti{Kernel smoothers and bootstrapping for semiparametric mixed effects models.}
\itemso{J. Multivariate Anal. 114, 288-302 (2013).}
\itemab
Summary: While today linear mixed effects models are frequently used tools in different fields of statistics, in particular for studying data with clusters, longitudinal or multi-level structures, the nonparametric formulation of mixed effects models is still quite recent. In this paper we discuss and compare different nonparametric estimation methods. In this context we introduce a computationally inexpensive bootstrap method, which is used to estimate local mean squared errors, to construct confidence intervals and to find locally optimal smoothing parameters. The theoretical considerations are accompanied by the provision of algorithms and simulation studies of the finite sample behavior of the methods. We show that our confidence intervals have good coverage probabilities, and that our bandwidth selection method succeeds to minimize the mean squared error for the nonparametric function locally.
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\itemcc{62G08 62G09 65C60}
\itemut{non- and semiparametric models; bootstrap inference; bandwidth choice; small area statistics}
\itemli{doi:10.1016/j.jmva.2012.08.005}
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