A nonparametric Bayesian test for detecting the difference in location parameters

Sunil Mathur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In biomedical studies, detecting the changes in a response distribution under different testing conditions is one of the important issues. For example, increase in dose level may lead to increase or decrease in the gene expression level. To address this issue, we propose a nonparametric Bayesian test for testing the difference in location when samples are collected under two different conditions. We apply Dirichlet process priors to estimate the probabilities, which imply constraint on cumulative distribution functions of occurrence evaluated at cut-off value that partitions the expression range of that gene into two intervals. The proposed test can be easily extended for multiple samples comparisons in gene expression analysis.

Original languageEnglish (US)
Title of host publicationBayesian Inference and Maximum Entropy Methods in Science and Engineering - 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
Pages382-388
Number of pages7
DOIs
StatePublished - 2009
Externally publishedYes
Event29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering - Oxford, MS, United States
Duration: Jul 5 2009Jul 10 2009

Publication series

NameAIP Conference Proceedings
Volume1193
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Other

Other29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
Country/TerritoryUnited States
CityOxford, MS
Period7/5/097/10/09

Keywords

  • Dirichlet process
  • Gene expression
  • Location
  • Nonparametric Bayes

ASJC Scopus subject areas

  • General Physics and Astronomy

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