Journal ArticleDOI
Nonparametric Bayesian survival analysis using mixtures of Weibull distributions
TLDR
This work model the survival distribution employing a flexible Dirichlet process mixture, with a Weibull kernel, that yields rich inference for several important functionals and develops a method for hazard function estimation.About:
This article is published in Journal of Statistical Planning and Inference.The article was published on 2006-03-01. It has received 94 citations till now. The article focuses on the topics: Dirichlet process & Dirichlet distribution.read more
Citations
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Journal ArticleDOI
Bayesian Survival Analysis.
Journal ArticleDOI
Computational Pathology: Challenges and Promises for Tissue Analysis
TL;DR: This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.
Journal ArticleDOI
Computational pathology: challenges and promises for tissue analysis.
TL;DR: In this paper, the authors report on state-of-the-art of computational pathology workflows and discuss future research directions in this emergent field of medical informatics and diagnostic machine learning.
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A Bayesian Nonparametric Approach to Inference for Quantile Regression
Matt Taddy,Athanasios Kottas +1 more
TL;DR: This paper developed a Bayesian method for nonparametric model-based quantile regression, which involves flexible Dirichlet process mixture models for the joint distribution of the response and the covariates.
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Bayesian mixture modeling for spatial Poisson process intensities, with applications to extreme value analysis
Athanasios Kottas,Bruno Sansó +1 more
TL;DR: In this article, a nonparametric mixture model for spatial point patterns is proposed, which is based on modeling a density function, defined on this bounded region, that is directly related with the intensity function of the Poisson process.
References
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Journal ArticleDOI
A Bayesian Analysis of Some Nonparametric Problems
TL;DR: In this article, a class of prior distributions, called Dirichlet process priors, is proposed for nonparametric problems, for which treatment of many non-parametric statistical problems may be carried out, yielding results that are comparable to the classical theory.
Book
Statistical Models and Methods for Lifetime Data
TL;DR: Inference procedures for Log-Location-Scale Distributions as discussed by the authors have been used for estimating likelihood and estimating function methods. But they have not yet been applied to the estimation of likelihood.
Journal ArticleDOI
Statistical Models and Methods for Lifetime Data
TL;DR: This book describes and illustrates how to compute a simple “naive” variance estimate and con dence intervals that would be correct under the assumption of an underlying nonhomogeneous Poisson process model.
Journal ArticleDOI
Bayesian Density Estimation and Inference Using Mixtures
Michael Escobar,Mike West +1 more
TL;DR: In this article, the authors describe and illustrate Bayesian inference in models for density estimation using mixtures of Dirichlet processes and show convergence results for a general class of normal mixture models.
Journal ArticleDOI
Statistical Models and Methods for Lifetime Data.
Brian Francis,Jerald F. Lawless +1 more