"Rich Mixtures for Multidimensional Smoothing"
joint with Bill Szewczyk
Abstract:
Mixture models, which include kernel estimators, are widely used to
model complex densities; however, one is faced with the challenge of
determining an appropriate number of components. Kernel methods are
a special case of mixture models but are generally viewed as rather
susceptible to the curse of dimensionality. Locally adaptive
kernel estimators are highly desirable in multiple dimensions but
few practical algorithms exist for such estimation. Thus mixture
models may be viewed as a more parsimonious kernel estimator but
without trying to get the number of components exactly correct.
In this talk we describe how a mixture estimate may be calibrated
by starting with a kernel estimator. The IPRA algorithm sequentially
fits mixture models.