The Rasch sampler is an efficient algorithm to sample binary matrices with given marginal sums. It is a Markov chain Monte Carlo (MCMC) algorithm. The program can handle matrices of up to 1024 rows and 64 columns. A special option allows to sample square matrices with given marginals and fixed main diagonal, a problem prominent in social network analysis. In all cases the stationary distribution is uniform. The user has control on the serial dependency
Analyzing latent variables is becoming more and more important in several fields, such as clinical r...
A method for estimating the parameters of the Rasch model is examined. The unknown quantities in thi...
The small scale applicability of Rasch estimates was investigated under simulated conditions of gue...
The Rasch sampler is an efficient algorithm to sample binary matrices with given marginal sums. It i...
The Rasch sampler is an efficient algorithm to sample binary matrices with given marginal sums. It i...
In their seminal work on characterizing the manifest probabilities of latent trait models, Cressie a...
In their seminal work on characterizing the manifest probabilities of latent trait models, Cressie a...
The article studies distributions of doubly infinite binary matrices with exchangeable rows and colu...
A short review of the different estimation procedures that have been used in association with the Ra...
For item responses fitting the Rasch model, the assumptions under-lying the Mokken model of double m...
Many statistical tests are designed to test the different assumptions of the Rasch model, but only f...
In order to obtain conditional maximum likelihood estimates, the conditioning constants are needed. ...
Markov chain Monte Carlo methods, in particular, the Gibbs sampler, are widely used algorithms both ...
In this paper, we study the Gibbs sampler algorithm and explore some of its applications. First, we ...
A new sampling design for populations whose units can be arranged as an matrix is proposed. The sam...
Analyzing latent variables is becoming more and more important in several fields, such as clinical r...
A method for estimating the parameters of the Rasch model is examined. The unknown quantities in thi...
The small scale applicability of Rasch estimates was investigated under simulated conditions of gue...
The Rasch sampler is an efficient algorithm to sample binary matrices with given marginal sums. It i...
The Rasch sampler is an efficient algorithm to sample binary matrices with given marginal sums. It i...
In their seminal work on characterizing the manifest probabilities of latent trait models, Cressie a...
In their seminal work on characterizing the manifest probabilities of latent trait models, Cressie a...
The article studies distributions of doubly infinite binary matrices with exchangeable rows and colu...
A short review of the different estimation procedures that have been used in association with the Ra...
For item responses fitting the Rasch model, the assumptions under-lying the Mokken model of double m...
Many statistical tests are designed to test the different assumptions of the Rasch model, but only f...
In order to obtain conditional maximum likelihood estimates, the conditioning constants are needed. ...
Markov chain Monte Carlo methods, in particular, the Gibbs sampler, are widely used algorithms both ...
In this paper, we study the Gibbs sampler algorithm and explore some of its applications. First, we ...
A new sampling design for populations whose units can be arranged as an matrix is proposed. The sam...
Analyzing latent variables is becoming more and more important in several fields, such as clinical r...
A method for estimating the parameters of the Rasch model is examined. The unknown quantities in thi...
The small scale applicability of Rasch estimates was investigated under simulated conditions of gue...