Coupling optional Pólya trees and the two sample problem

Statistics – Methodology

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

44 pages, 6 figures

Scientific paper

Testing and characterizing the difference between two data samples is of fundamental interest in statistics. Existing methods such as Kolmogorov-Smirnov and Cramer-von-Mises tests do not scale well as the dimensionality increases and provides no easy way to characterize the difference should it exist. In this work, we propose a theoretical framework for inference that addresses these challenges in the form of a prior for Bayesian nonparametric analysis. The new prior is constructed based on a random-partition-and-assignment procedure similar to the one that defines the standard optional P\'olya tree distribution, but has the ability to generate multiple random distributions jointly. These random probability distributions are allowed to "couple", that is to have the same conditional distribution, on subsets of the sample space. We show that this "coupling optional P\'olya tree" prior provides a convenient and effective way for both the testing of two sample difference and the learning of the underlying structure of the difference. In addition, we discuss some practical issues in the computational implementation of this prior and provide several numerical examples to demonstrate its work.

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

Coupling optional Pólya trees and the two sample problem 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 Coupling optional Pólya trees and the two sample problem, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Coupling optional Pólya trees and the two sample problem will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-146733

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