Alternating kernel and mixture density estimates
Carey E. Priebe, David J. Marchette · Computational Statistics & Data Analysis · 2000
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.