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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Published on: December 10, 2012

Bayesian hierarchical functional data analysis via contaminated informative priors.

Bruno Scarpa1, David B Dunson

  • 1Department of Statistical Sciences, University of Padua, 241 Padua, Italy. scarpa@stat.unipd.it

Biometrics
|January 29, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible Bayesian approach for analyzing functional data, particularly menstrual cycle temperature curves. It effectively incorporates prior knowledge while allowing for unexpected variations in unhealthy cycles.

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Area of Science:

  • Statistics
  • Biostatistics
  • Functional Data Analysis

Background:

  • Flexible methods for functional data analysis are crucial when prior information is limited.
  • Modeling temperature curves in the menstrual cycle requires accounting for unknown mean and distribution.
  • Hierarchical functional data analysis presents unique challenges in incorporating prior knowledge.

Purpose of the Study:

  • To propose a flexible semiparametric Bayesian approach for hierarchical functional data analysis.
  • To incorporate prior information effectively into functional data models.
  • To model temperature curves in the menstrual cycle, allowing for deviations in unhealthy cycles.

Main Methods:

  • A mixture model combining a parametric hierarchical model with a nonparametric contamination component.
  • Utilizing a functional Dirichlet process to characterize the contamination.
  • Developing methods for posterior computation in this semiparametric Bayesian framework.

Main Results:

  • The proposed approach successfully incorporates prior information into functional data analysis.
  • The contamination component effectively captures unanticipated curve shapes, as demonstrated in unhealthy menstrual cycles.
  • The method was applied to real-world data from a European fecundability study.

Conclusions:

  • The developed semiparametric Bayesian approach offers a flexible way to analyze hierarchical functional data.
  • This method enhances modeling capabilities by integrating prior knowledge and accommodating deviations.
  • The application to menstrual cycle data highlights its utility in biological and health-related studies.