Consistency of Random Survival Forests (2008)
Ishwaran, Hemant, Kogalur, Udaya B.
We prove uniform consistency of Random Survival Forests (RSF), a newly introduced forest ensemble learner for analysis of right-censored survival data. Consistency is proven under general splitting...
Random survival forests (2008)
Ishwaran, Hemant, Kogalur, Udaya B., Blackstone, Eugene H., Lauer, Michael S.
We introduce random survival forests, a random forests method for the analysis of right-censored survival data. New survival splitting rules for growing survival trees are introduced, as is a new...
Atreja, Ashish, Mehta, Neil B, Jain, Anil K, Harris, CM, Ishwaran, Hemant, Avital, Michel, ...
Abstract Background Healthcare institutions spend enormous time and effort to train their workforce. Web-based training can potentially streamline this process. However the deployment of web-based...
Orthogonalized smoothing for rescaled spike and slab models (2008)
Ishwaran, Hemant, Papana, Ariadni
Rescaled spike and slab models are a new Bayesian variable selection method for linear regression models. In high dimensional orthogonal settings such models have been shown to possess optimal model...
Variable importance in binary regression trees and forests (2007)
We characterize and study variable importance (VIMP) and pairwise variable associations in binary regression trees. A key component involves the node mean squared error for a quantity we refer to as...
BAMarray™: Java software for Bayesian analysis of variance for microarray data (2006)
Ishwaran, Hemant, Rao, J Sunil, Kogalur, Udaya B
Abstract Background DNA microarrays open up a new horizon for studying the genetic determinants of disease. The high throughput nature of these arrays creates an enormous wealth of information, but...
BAMarraytrade mark: Java software for Bayesian analysis of variance for microarray data. (2006)
Ishwaran, Hemant, Rao, J Sunil, Kogalur, Udaya B
BACKGROUND: DNA microarrays open up a new horizon for studying the genetic determinants of disease. The high throughput nature of these arrays creates an enormous wealth of information, but also...
Spike and slab variable selection: Frequentist and Bayesian strategies (2005)
Ishwaran, Hemant, Rao, J. Sunil
Variable selection in the linear regression model takes many apparent faces from both frequentist and Bayesian standpoints. In this paper we introduce a variable selection method referred to as a...
Spike and slab variable selection: Frequentist and Bayesian strategies (2005)
Ishwaran, Hemant, Rao, J. Sunil
Variable selection in the linear regression model takes many apparent faces from both frequentist and Bayesian standpoints. In this paper we introduce a variable selection method referred to as a...
Discussion of "Least angle regression" by Efron et al (2004)
Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]
A Recursive Method For Functionals (2003)
Dragan Banjevic, Hemant Ishwaran, Mahmoud Zarepour
this paper we describe a simple recursive method requiring only the use of conditional probability that is useful for characterizing Poisson process functionals. Some applications of this technique...
Random probability measures via Polya sequences: revisiting the Blackwell-MacQueen urn scheme (2003)
Ishwaran, Hemant, Zarepour, Mahmoud
Sufficient conditions are developed for a class of generalized Polya urn schemes ensuring exchangeability. The extended class includes the Blackwell-MacQueen Polya urn and the urn schemes for the...
Hemant Ishwaran, Lancelot F. James
INTRODUCTION Consider the nite mixture problem where we wish to estimate Q 0 , an unknown nite mixing distribution with d atoms. We assume that d < 1 is unknown but that a nite upper bound N is known...
Hemant Ishwaran, Lancelot F. James, Jiayang Sun
INTRODUCTION Consider the # nite mixture problem where we wish to estimate Q 0 , an unknown # nite mixing distribution with d atoms. We assume that d < is unknown but that a # nite upper bound N is...
Gibbs Sampling Methods for Stick-Breaking Priors Hemant Ishwaran and Lancelot F. James (2003)
Hemant Ishwaran, Lancelot F. James
this article we present two general types of Gibbs samplers that can be used to # t posteriors of Bayesian hierarchical models based on stick-breaking priors. The # rst type of Gibbs sampler,...
Hemant Ishwaran, Lancelot F. James
this paper will be to develop new surrounding theory for the hierarchical model (7) and show how these may be used to develop computational algorithms for computing posterior quantities. Our...
Distributed Monte Carl, Hemant Ishwaran, Glen Takahara
This articleintrok6flI a new classo independent and identically distributed (iid) Mod) Carlo algoflJflflz which can be usedfo inference in semiparametric linear mixedmoI els under minimalassumptiofl...
Approximate Dirichlet Process Computing in (2002)
Hemant Ishwaran, Lancelot F. James
This article looks at the use of a new Gibbs sampling method described by Ishwaran and Zarepour (2000) and Ishwaran and James (2001), which differs from the Gibbs sampling approaches mentioned above,...
A recursive method for functionals of Poisson processes (2002)
Banjevic, Dragan, Ishwaran, Hemant, Zarepour, Mahmoud
Functionals of Poisson processes arise in many statistical problems. They appear in problems involving heavy-tailed distributions in the study of limiting processes, while in Bayesian nonparametric...
Coronary Risk Prediction by Logical Analysis of Data (2002)
Sorin Alexe, Eugene Blackstone, Peter L. Hammer, Hemant Ishwaran, Michael S. Lauer
The objective of this study was to distinguish within a population of patients with known or suspected coronary artery disease groups at high and at low mortality rates. The study was based on...
Manuscript: CJS 004JAN01 (2002)
Hemant Ishwaran, Mahmoud Zarepour
The authors consider various sum-representations for the Dirichlet process when considered as a random probability measure. Two representations considered in detail are the Ferguson (1973) gamma...
Dirichlet Prior Sieves In Finite Normal Mixtures (2002)
Hemant Ishwaran, Mahmoud Zarepour
The use of a finite dimensional Dirichlet prior in the finite normal mixture model has the effect of acting like a Bayesian method of sieves. Posterior consistency is directly related to the...
Hemant Ishwaran, Lancelot F. James
INTRODUCTION The nite normal mixture model has been the subject of much research interest from a Bayesian perspective. See for example, Ferguson (1983), Escobar (1988, 1994), Diebolt and Robert...
Hemant Ishwaran, Constantine A. Gatsonis
The authors discuss a general class of hierarchical ordinal regression models that includes both location and scale parameters, allows link functions to be selected adaptively as finite mixtures of...
this paper will be to study the performance of hybrid Monte Carlo in logistic regression problems with quasicomplete separation. The variation of hybrid Monte Carlo that we study is based on the...
Inference For The Random Effects In Bayesian Generalized Linear Mixed Models (2000)
The use of a truncation approximation to the Dirichlet process is utilized in a Gibbs sampling scheme for fitting a semiparametric generalized linear mixed model. A novel aspect of this approach is...
Gibbs Sampling Methods for Stick-Breaking Priors (2000)
Hemant Ishwaran, Lancelot F. James
this paper we present two general types of Gibbs samplers that can be used to fit posteriors of Bayesian hierarchical models based on stick-breaking priors. The first type of Gibbs sampler, referred...
Hemant Ishwaran, Constantine A. Gatsonis
The authors discuss a general class of hierarchical ordinal regression models that includes both location and scale parameters, allows link functions to be selected adaptively as finite mixtures of...
Hemant Ishwaran, Constantine A. Gatsonis
The authors discuss a general class of hierarchical ordinal regression models that includes both location and scale parameters, allows link functions to be selected adaptively as finite mixtures of...
this paper will be to study the performance of hybrid Monte Carlo in logistic regression problems with quasicomplete separation. The variation of hybrid Monte Carlo that we study is based on the...
Information in semiparametric mixtures of exponential families (1999)
Z In a class of semiparametric mixture models, the score function (and consequently the effective information) for a finite-dimensional parameter can be made arbitrarily small depending upon the...
Advances in Markov chain Monte Carlo MCMC methods now make it computationally feasible and relatively straightforward to apply the Dirichlet process prior in a wide range of Bayesian nonparametric...
Identifiability and rates of estimation for scale parameters in location mixture models (1996)
In this paper we consider the problem of identifiability and estimation for the scale parameter $\theta$ in the location mixture model $\theta (X + Y)$, where X has a known distribution independent...
Uniform rates of estimation in the semiparametric Weibull mixture model (1996)
This paper presents a uniform estimator for a finite-dimensional parameter in the semiparametric Weibull mixture model. The rates achieved by the estimator hold uniformly over shrinking sequences of...
Rates of convergence in semiprametric mixture models [microform]. (1994)
University Microfilms order no. 9433711.
Rates of convergence in semiparametric mixture models / (1993)
Thesis (Ph. D.)--Yale University, 1993.
Lung metastasis genes couple breast tumor size and metastatic spread
Minn, Andy J., Gupta, Gaorav P., Padua, David, Bos, Paula, Nguyen, Don X., Nuyten, Dimitry, ...
The association between large tumor size and metastatic risk in a majority of clinical cancers has led to questions as to whether these observations are causally related or whether one is simply a...
AN IN-DEPTH LOOK AT HIGHEST POSTERIOR MODEL SELECTION
Dey, Tanujit, Ishwaran, Hemant, Rao, J. Sunil
We consider the properties of the highest posterior probability model in a linear regression setting. Under a spike and slab hierarchy we find that although highest posterior model selection is total...