Abstract
We describe and investigate a data-driven procedure for obtaining parsimonious mixture model estimates or, conversely, kernel estimates with data-driven local smoothing properties. The main idea is to obtain a semiparametric estimate by alternating between the parametric and nonparametric viewpoints.
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