Minimax hypothesis testing for curve registration

Details Minimax hypothesis testing for curve registration Minimax hypothesis testing for curve registration

2011-09-06

arxiv.org/abs/1109.1244v1

Mathematics

Statistics Theory

Scientific paper

This paper is concerned with the problem of goodness-of-fit for curve registration, and more precisely for the shifted curve model, whose application field reaches from computer vision and road traffic prediction to medicine. We give bounds for the asymptotic minimax separation rate, when the functions in the alternative lie in Sobolev balls and the separation from the null hypothesis is measured by the l2-norm. We use the generalized likelihood ratio to build a nonadaptive procedure depending on a tuning parameter, which we choose in an optimal way according to the smoothness of the ambient space. Then, a Bonferroni procedure is applied to give an adaptive test over a range of Sobolev balls. Both achieve the asymptotic minimax separation rates, up to possible logarithmic factors.

Affiliated with

Also associated with

No associations

Rating

Minimax hypothesis testing for curve registration 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 Minimax hypothesis testing for curve registration, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Minimax hypothesis testing for curve registration will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-94100