Add to ORKGIn recent years, there has been growing interest in statistical models incorporating inequality constraints on model parameters. This is because the omnibus hypotheses can be replaced by more specific inequality constrained hypotheses. In this thesis several models that deal with categorical data are extended to be able to handle inequality constraints on the model parameters
Several issues are discussed when testing inequality constrained hypotheses using a Bayesian approac...
Bayesian optimization is a powerful frame-work for minimizing expensive objective functions while us...
In the social sciences we are often interested in comparing models specified by parametric equality ...
In recent years, there has been growing interest in statistical models incorporating inequality cons...
Researchers often have one or more theories or expectations with respect to the outcome of their emp...
Bayesian evaluation of inequality constrained hypotheses enables researchers to investigate their ex...
This dissertation deals with normal linear models with inequality constraints among model parameters...
In econometric models, sign or inequality constraints on parameters arise in a wide variety of appli...
Abstract: Constrained parameter problems arise in a wide variety of applications. This article deals...
The expectations that researchers have about the structure in the data can often be formulated in te...
In this paper, we consider the multicollinearity problem in the gamma regression model when model pa...
Researchers in the behavioral and social sciences often have expectations that can be expressed in ...
We develop a Bayesian framework for making inference on a class of marginal models for categorical ...
An encompassing prior (EP) approach to facilitate Bayesian model selection for nested models with in...
Univariate and multivariate general linear regression models, subject to linear inequality constrain...
Several issues are discussed when testing inequality constrained hypotheses using a Bayesian approac...
Bayesian optimization is a powerful frame-work for minimizing expensive objective functions while us...
In the social sciences we are often interested in comparing models specified by parametric equality ...
In recent years, there has been growing interest in statistical models incorporating inequality cons...
Researchers often have one or more theories or expectations with respect to the outcome of their emp...
Bayesian evaluation of inequality constrained hypotheses enables researchers to investigate their ex...
This dissertation deals with normal linear models with inequality constraints among model parameters...
In econometric models, sign or inequality constraints on parameters arise in a wide variety of appli...
Abstract: Constrained parameter problems arise in a wide variety of applications. This article deals...
The expectations that researchers have about the structure in the data can often be formulated in te...
In this paper, we consider the multicollinearity problem in the gamma regression model when model pa...
Researchers in the behavioral and social sciences often have expectations that can be expressed in ...
We develop a Bayesian framework for making inference on a class of marginal models for categorical ...
An encompassing prior (EP) approach to facilitate Bayesian model selection for nested models with in...
Univariate and multivariate general linear regression models, subject to linear inequality constrain...
Several issues are discussed when testing inequality constrained hypotheses using a Bayesian approac...
Bayesian optimization is a powerful frame-work for minimizing expensive objective functions while us...
In the social sciences we are often interested in comparing models specified by parametric equality ...