Conditional Maximum Likelihood Estimation in Polytomous Rasch Models Using SAS

%lrasch mml: A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models

Item response theory models are often applied when a number items are used to measure a unidimensional latent variable. Originally proposed and used within educational research, they are also used when focus is on physical functioning or psychological wellbeing. Modern applications often need more general models, typically models for multidimensional latent variables or longitudinal models for ...

A Nonparametric Maximum Likelihood Estimation of Conditional Moment Restriction Models

This paper studies estimation of a conditional moment restriction model using the nonparametric maximum likelihood approach proposed by Gallant and Nychka (1987). Under some sufficient conditions, we show that the estimator of some finite dimensional parameters is asymptotically normally distributed and attains the semiparametric efficiency bound and that the estimator of the density function i...

Joint and Conditional Maximum Likelihood Estimation for the Rasch Model for Binary Responses

The usefulness of joint and conditional maximum-likelihood is considered for the Rasch model under realistic testing conditions in which the number of examinees is very large and the number is items is relatively large. Conditions for consistency and asymptotic normality are explored, effects of model error are investigated, measures of prediction are estimated, and generalized residuals are de...

Maximum likelihood estimation and model comparison of nonlinear structural equation models with continuous and polytomous variables

Recently, it is recognized that nonlinear relationships among latent variables in a structural equation model are important. In this article, maximum likelihood (ML) analysis of a general nonlinear structural equation model that contains 5xed covariates in the measurement equation and the nonlinear structural equation is investigated. A MCEM algorithm is implemented to obtain the ML estimates, ...

Quasi-Maximum Likelihood Estimation For Latent Variable Models With Mixed Continuous And Polytomous Data