[[abstract]]We describe a Bayesian framework for shape-restricted regression in which the prior is given by Bernstein polynomials. We present consistency theorems concerning the posterior distribution in this Bayesian approach. This study includes monotone regression and a few other shape-restricted regressions.[[notice]]補正完畢[[booktype]]紙本[[booktype]]電子版[[countrycodes]]SG
In this paper, the polynomial approximation of distributed lags is investigated within the framework...
Calculating regression under shape constraints is a problem addressed by statisticians since long. T...
<p>We propose a novel class of probability models for sets of predictor-dependent probability distri...
[[abstract]]We describe a Bayesian framework for shape-restricted regression in which the prior is g...
One of the standard problems in statistics consists of determining the relationship between a respon...
Random Bernstein polynomials induces a probability measure on the space of multivariate density func...
Nous étudions la régression bayésienne sous contraintes de régularité et de forme. Pour cela,on cons...
A Bernstein prior is a probability measure on the space of all the distribution functions on [0,1]....
Our goal is inference for shape-restricted functions. Our functional form consists of finite linear ...
A Bayesian method for regression under several types of constraints is proposed. The constraints can...
Bayesian consistency is an important issue in the context of non- parametric problems. The posterior...
This thesis deals with a number of statistical problems where either censoringor shape-constraints p...
[[abstract]]Bayesian survival analysis of right-censored survival data is studied using priors on Be...
We consider Bayesian inference in the linear regression problem with an unknown error distribution t...
Statistical inference on infinite-dimensional parameters in Bayesian framework is investigated. The ...
In this paper, the polynomial approximation of distributed lags is investigated within the framework...
Calculating regression under shape constraints is a problem addressed by statisticians since long. T...
<p>We propose a novel class of probability models for sets of predictor-dependent probability distri...
[[abstract]]We describe a Bayesian framework for shape-restricted regression in which the prior is g...
One of the standard problems in statistics consists of determining the relationship between a respon...
Random Bernstein polynomials induces a probability measure on the space of multivariate density func...
Nous étudions la régression bayésienne sous contraintes de régularité et de forme. Pour cela,on cons...
A Bernstein prior is a probability measure on the space of all the distribution functions on [0,1]....
Our goal is inference for shape-restricted functions. Our functional form consists of finite linear ...
A Bayesian method for regression under several types of constraints is proposed. The constraints can...
Bayesian consistency is an important issue in the context of non- parametric problems. The posterior...
This thesis deals with a number of statistical problems where either censoringor shape-constraints p...
[[abstract]]Bayesian survival analysis of right-censored survival data is studied using priors on Be...
We consider Bayesian inference in the linear regression problem with an unknown error distribution t...
Statistical inference on infinite-dimensional parameters in Bayesian framework is investigated. The ...
In this paper, the polynomial approximation of distributed lags is investigated within the framework...
Calculating regression under shape constraints is a problem addressed by statisticians since long. T...
<p>We propose a novel class of probability models for sets of predictor-dependent probability distri...