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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Published on: October 23, 2020

Random Partition Models with Regression on Covariates.

Peter Müller1, Fernando Quintana

  • 1M.D. Anderson Cancer Center, Houston, TX.

Journal of Statistical Planning and Inference
|August 10, 2010
PubMed
Summary
This summary is machine-generated.

This study reviews random partition models for clustering, focusing on extensions that incorporate covariates for informed clustering. It discusses methods for implementing these covariate-based random partition models.

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Area of Science:

  • Statistics
  • Machine Learning

Background:

  • Nonparametric Bayesian inference frequently utilizes random partition models for clustering experimental units.
  • These models provide a probabilistic framework for grouping data points.

Purpose of the Study:

  • To review fundamental random partition models.
  • To explore extensions of these models that integrate covariates for enhanced clustering.
  • To discuss implementation strategies for covariate-indexed random clustering models.

Main Methods:

  • Review of existing literature on random partition models.
  • Focus on covariate-indexed random clustering models, framed as regression models.
  • Discussion of alternative implementation approaches, including extensions of product partition models.

Main Results:

  • Identified popular basic constructions of random partition models.
  • Highlighted covariate-based extensions for a priori informed clustering.
  • Presented several approaches applicable to covariate-based inference, even if not originally designed for it.

Conclusions:

  • Covariates can significantly inform clustering in nonparametric Bayesian inference.
  • Various methods exist for implementing covariate-indexed random partition models.
  • Recent extensions, particularly of product partition models, offer promising avenues for covariate-based clustering.