Density estimation on multivariate censored data with optional Pólya tree

Junhee Seok, Lu Tian, Wing H. Wong

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Analyzing the failure times of multiple events is of interest in many fields. Estimating the joint distribution of the failure times in a non-parametric way is not straightforward because some failure times are often right-censored and only known to be greater than observed follow-up times. Although it has been studied, there is no universally optimal solution for this problem. It is still challenging and important to provide alternatives that may be more suitable than existing ones in specific settings. Related problems of the existing methods are not only limited to infeasible computations, but also include the lack of optimality and possible non-monotonicity of the estimated survival function. In this paper, we proposed a non-parametric Bayesian approach for directly estimating the density function of multivariate survival times, where the prior is constructed based on the optional Pólya tree. We investigated several theoretical aspects of the procedure and derived an efficient iterative algorithm for implementing the Bayesian procedure. The empirical performance of the method was examined via extensive simulation studies. Finally, we presented a detailed analysis using the proposed method on the relationship among organ recovery times in severely injured patients. From the analysis, we suggested interesting medical information that can be further pursued in clinics.

Original languageEnglish
Pages (from-to)182-195
Number of pages14
JournalBiostatistics
Volume15
Issue number1
DOIs
Publication statusPublished - 2014 Jan

Keywords

  • Multivariate survival analysis
  • Non-parametric Bayesian
  • Optional Pólya tree

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Density estimation on multivariate censored data with optional Pólya tree'. Together they form a unique fingerprint.

  • Cite this