Pages that link to "Item:Q2466476"

The following pages link to Describing disability through individual-level mixture models for multivariate binary data (Q2466476):

Displaying 37 items.

• Obtaining cell counts for contingency tables from rounded conditional frequencies (Q322408) (← links)
• Rapid calculation of exact cell bounds for contingency tables from conditional frequencies (Q337192) (← links)
• Clustering South African households based on their asset status using latent variable models (Q400600) (← links)
• Maximum likelihood estimation in log-linear models (Q447845) (← links)
• Bayesian models and methods in public policy and government settings (Q449814) (← links)
• A hierarchical Bayesian approach to record linkage and population size problems (Q641103) (← links)
• Copula Gaussian graphical models and their application to modeling functional disability data (Q641151) (← links)
• Mixtures of weighted distance-based models for ranking data with applications in political studies (Q693262) (← links)
• Logit tree models for discrete choice data with application to advice-seeking preferences among Chinese Christians (Q736666) (← links)
• A deep learning algorithm for high-dimensional exploratory item factor analysis (Q823855) (← links)
• Mixture of latent trait analyzers for model-based clustering of categorical data (Q892805) (← links)
• Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions (Q958332) (← links)
• The utility of reliability and survival (Q965127) (← links)
• Hierarchical relational models for document networks (Q977628) (← links)
• A mixture of experts model for rank data with applications in election studies (Q999671) (← links)
• Assessing multivariate predictors of financial market movements: A latent factor framework for ordinal data (Q1018618) (← links)
• On the use of bootstrap with variational inference: theory, interpretation, and a two-sample test example (Q1624811) (← links)
• A general method for robust Bayesian modeling (Q1631602) (← links)
• Chimeral clustering (Q2129309) (← links)
• Multivariate mixed membership modeling: inferring domain-specific risk profiles (Q2135361) (← links)
• Partial-mastery cognitive diagnosis models (Q2247501) (← links)
• Longitudinal mixed membership trajectory models for disability survey data (Q2258577) (← links)
• Bayesian nonparametric disclosure risk estimation via mixed effects log-linear models (Q2349591) (← links)
• Exponential family mixed membership models for soft clustering of multivariate data (Q2418280) (← links)
• Distance-based tree models for ranking data (Q2445618) (← links)
• Identifying Latent Structures in Restricted Latent Class Models (Q4559708) (← links)
• Estimating Identification Disclosure Risk Using Mixed Membership Models (Q4904716) (← links)
• Simplex Factor Models for Multivariate Unordered Categorical Data (Q4916469) (← links)
• A fuzzy clustering approach to evaluate individual competencies from REFLEX data (Q5138725) (← links)
• Medical overpayment estimation: A Bayesian approach (Q5142179) (← links)
• Using imputation and mixture model approaches to integrate multi‐state capture–recapture models with assignment information (Q5170199) (← links)
• Statistical Inference in a Directed Network Model With Covariates (Q5231512) (← links)
• Partially Ordered Mixed Hidden Markov Model for the Disablement Process of Older Adults (Q5327260) (← links)
• Semiparametric finite mixture of regression models with Bayesian P-splines (Q6050760) (← links)
• Finding mixed memberships in categorical data (Q6562296) (← links)
• Joint mixed membership modeling of multivariate longitudinal and survival data for learning the individualized disease progression (Q6616333) (← links)
• Latent Dirichlet Analysis of Categorical Survey Responses (Q6620849) (← links)