Recursive estimation of the conditional geometric median in Hilbert spaces

Mathematics – Statistics Theory

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

A recursive estimator of the conditional geometric median in Hilbert spaces is studied. It is based on a stochastic gradient algorithm whose aim is to minimize a weighted L1 criterion and is consequently well adapted for robust online estimation. The weights are controlled by a kernel function and an associated bandwidth. Almost sure convergence and L2 rates of convergence are proved under general conditions on the conditional distribution as well as the sequence of descent steps of the algorithm and the sequence of bandwidths. Asymptotic normality is also proved for the averaged version of the algorithm with an optimal rate of convergence. A simulation study confirms the interest of this new and fast algorithm when the sample sizes are large. Finally, the ability of these recursive algorithms to deal with very high-dimensional data is illustrated on the robust estimation of television audience profiles conditional on the total time spent watching television over a period of 24 hours.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Recursive estimation of the conditional geometric median in Hilbert spaces 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 Recursive estimation of the conditional geometric median in Hilbert spaces, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Recursive estimation of the conditional geometric median in Hilbert spaces will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-287126

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.