Add to ORKGThe two-point mixture index of fit enjoys some desirable features in model fit assessment and model selection, however, a need exists for efficient computational strategies. Applying an NLP algorithm, a program using the SAS matrix language is presented to estimate the two-point index of fit for two-class LCA models with dichotomous response variables. The program offers a tool to compute π ∗ for twoclass models and it also provides an alternative program for conducting latent class analysis with SAS. This study builds a foundation for further research on computational approaches for M-class models
Mixture models have been around for over 150 years, and they are found in many branches of statistic...
This paper describes graphical methods for multiple-response data within the framework of the multiv...
We consider the fitting of normal mixture models to multivariate data, using maximum likelihood via ...
The two-point mixture index of fit enjoys some desirable features in model fit assessment and model ...
Linear mixed models provide a flexible, intuitive method for analyzing repeated-measures data when t...
This simulation study examines the performance of fit indices commonly used by applied researchers i...
Abstract from short.pdf file.Dissertation supervisor: Dr. Douglas Steinley.Includes vita.In the psyc...
Several competing computational techniques for dealing with sampling zeros were evaluated when estim...
<p>Note: CFA = Confirmatory Factor Analysis; AIC = Akaike Information Criteria; BIC = Bayesian Infor...
Taxometric and latent variable mixture models can aid in (1) determining whether the source of popul...
In many situations, an outcome of interest has a large number of zero outcomes and a group of nonzer...
PCIC_SAS is a SAS program for identifying optimal subsets of means based on independent groups. All ...
The No-U-Turn Sampler (NUTS) is a relatively new Markov chain Monte Carlo (MCMC) algorithm that avoi...
This Monte Carlo simulation study examined the performance of the most commonly used fit indices in ...
We develop a SAS macro and equivalent Stata programs that provide marginalized inference for semi-co...
Mixture models have been around for over 150 years, and they are found in many branches of statistic...
This paper describes graphical methods for multiple-response data within the framework of the multiv...
We consider the fitting of normal mixture models to multivariate data, using maximum likelihood via ...
The two-point mixture index of fit enjoys some desirable features in model fit assessment and model ...
Linear mixed models provide a flexible, intuitive method for analyzing repeated-measures data when t...
This simulation study examines the performance of fit indices commonly used by applied researchers i...
Abstract from short.pdf file.Dissertation supervisor: Dr. Douglas Steinley.Includes vita.In the psyc...
Several competing computational techniques for dealing with sampling zeros were evaluated when estim...
<p>Note: CFA = Confirmatory Factor Analysis; AIC = Akaike Information Criteria; BIC = Bayesian Infor...
Taxometric and latent variable mixture models can aid in (1) determining whether the source of popul...
In many situations, an outcome of interest has a large number of zero outcomes and a group of nonzer...
PCIC_SAS is a SAS program for identifying optimal subsets of means based on independent groups. All ...
The No-U-Turn Sampler (NUTS) is a relatively new Markov chain Monte Carlo (MCMC) algorithm that avoi...
This Monte Carlo simulation study examined the performance of the most commonly used fit indices in ...
We develop a SAS macro and equivalent Stata programs that provide marginalized inference for semi-co...
Mixture models have been around for over 150 years, and they are found in many branches of statistic...
This paper describes graphical methods for multiple-response data within the framework of the multiv...
We consider the fitting of normal mixture models to multivariate data, using maximum likelihood via ...