Adaptive density estimation for clustering with Gaussian mixtures

Details Adaptive density estimation for clustering with Gaussian mixtures Adaptive density estimation for clustering with Gaussian mixtures

2011-03-22

arxiv.org/abs/1103.4253v2

Mathematics

Statistics Theory

Scientific paper

Gaussian mixture models are widely used to study clustering problems. These model-based clustering methods require an accurate estimation of the unknown data density by Gaussian mixtures. In Maugis and Michel (2009), a penalized maximum likelihood estimator is proposed for automatically selecting the number of mixture components. In the present paper, a collection of univariate densities whose logarithm is locally {\beta}-H\"older with moment and tail conditions are considered. We show that this penalized estimator is minimax adaptive to the {\beta} regularity of such densities in the Hellinger sense.

Affiliated with

Also associated with

No associations

Rating

Adaptive density estimation for clustering with Gaussian mixtures does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Adaptive density estimation for clustering with Gaussian mixtures, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Adaptive density estimation for clustering with Gaussian mixtures will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-48936