Add to ORKGIn a Bayesian framework, prior distributions on a space of nonparametric continuous distributions may be defined using Polya trees. This dissertation addresses statistical problems for which the Polya tree idea can be utilized to provide efficient and prac-tical methodological solutions. One problem considered is the estimation of risks, odds ratios, or other similar measures that are derived by specifying a threshold for an observed continuous vari-able. It has been previously shown that fitting a linear model to the continuous outcome under the assumption of a logistic error distribution leads to more efficient odds ratio estimates. We will show that deviations from the assumption of logistic error can result in great bias in odds ratio e...
We introduce approaches to performing Bayesian nonparametric statistical inference for distribution ...
<div><p>A flexible semiparametric odds ratio model has been proposed to unify and to extend both the...
This paper describes a general scheme for accomodating different types of conditional distributions ...
In a Bayesian framework, prior distributions on a space of nonparametric continuous distributions ma...
It has become more and more evident that under many circumstances assumptions used in parametric ana...
My dissertation considers three related topics involving censored or truncated survival data. All th...
We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial lik...
We consider Bayesian inference in the linear regression problem with an unknown error distribution t...
The logistic specification has been used extensively in non-Bayesian statistics to model the depende...
The number of categorical observations that are unique in a sample and also unique, or rare, in the...
According to the Bayesian theory, observations are usually considered to be part of an infinite sequ...
Parametric models such as the bi-normal have been widely used to analyse datafrom imperfect continuo...
208 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1993.We consider the problem of re...
Alternatives to the Dirichlet prior for multinomial probabilities are explored. The Dirichlet prior ...
Nonparametric and nonlinear measures of statistical dependence between pairs of random variables are...
We introduce approaches to performing Bayesian nonparametric statistical inference for distribution ...
<div><p>A flexible semiparametric odds ratio model has been proposed to unify and to extend both the...
This paper describes a general scheme for accomodating different types of conditional distributions ...
In a Bayesian framework, prior distributions on a space of nonparametric continuous distributions ma...
It has become more and more evident that under many circumstances assumptions used in parametric ana...
My dissertation considers three related topics involving censored or truncated survival data. All th...
We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial lik...
We consider Bayesian inference in the linear regression problem with an unknown error distribution t...
The logistic specification has been used extensively in non-Bayesian statistics to model the depende...
The number of categorical observations that are unique in a sample and also unique, or rare, in the...
According to the Bayesian theory, observations are usually considered to be part of an infinite sequ...
Parametric models such as the bi-normal have been widely used to analyse datafrom imperfect continuo...
208 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1993.We consider the problem of re...
Alternatives to the Dirichlet prior for multinomial probabilities are explored. The Dirichlet prior ...
Nonparametric and nonlinear measures of statistical dependence between pairs of random variables are...
We introduce approaches to performing Bayesian nonparametric statistical inference for distribution ...
<div><p>A flexible semiparametric odds ratio model has been proposed to unify and to extend both the...
This paper describes a general scheme for accomodating different types of conditional distributions ...