Remark on some conformally invariant integral equations: the method of moving spheres

We study some conformally invariant integral equations using the method of moving spheres

Conformally invariant fully nonlinear elliptic equations and isolated singularities

We study solutions to conformally invariant equations with isolated singularties.Comment: some improvement in exposition mad

A MATLAB Package for Markov Chain Monte Carlo with a Multi-Unidimensional IRT Model

Unidimensional item response theory (IRT) models are useful when each item is designed to measure some facet of a unified latent trait. In practical applications, items are not necessarily measuring the same underlying trait, and hence the more general multi-unidimensional model should be considered. This paper provides the requisite information and description of software that implements the Gibbs sampler for such models with two item parameters and a normal ogive form. The software developed is written in the MATLAB package IRTmu2no. The package is flexible enough to allow a user the choice to simulate binary response data with multiple dimensions, set the number of total or burn-in iterations, specify starting values or prior distributions for model parameters, check convergence of the Markov chain, as well as obtain Bayesian fit statistics. Illustrative examples are provided to demonstrate and validate the use of the software package.

Model Selection in Stochastic Frontier Analysis: Maize Production in Kenya

We demonstrate how a recently developed model selection procedure can be used to choose among competing stochastic frontier models where inefficiency depends on firm characteristics. We provide evidence on the power of this procedure. Moreover, we examine the effects of model choice on estimation results. We find that different models can lead to rather different magnitudes of the partial effects of the exogenous factors. However, because the model selection procedure gives an unambiguous choice of best model, and because bootstrapping indicates that the procedure is reliable, we conclude that it does not matter whether other models give different results.Research Methods/ Statistical Methods,

Markov Chain Monte Carlo Estimation of Normal Ogive IRT Models in MATLAB

Modeling the interaction between persons and items at the item level for binary response data, item response theory (IRT) models have been found useful in a wide variety of applications in various fields. This paper provides the requisite information and description of software that implements the Gibbs sampling procedures for the one-, two- and three-parameter normal ogive models. The software developed is written in the MATLAB package IRTuno. The package is flexible enough to allow a user the choice to simulate binary response data, set the number of total or burn-in iterations, specify starting values or prior distributions for model parameters, check convergence of the Markov chain, and obtain Bayesian fit statistics. Illustrative examples are provided to demonstrate and validate the use of the software package. The m-file v25i08.m is also provided as a guide for the user of the MCMC algorithms with the three dichotomous IRT models.