NDLI: Divisive Latent Class Modeling as a Density Estimation Method for Categorical Data

Content Provider	Springer Nature Link
Author	Palm, Daniël W. Ark, L. Andries Vermunt, Jeroen K.
Copyright Year	2016
Abstract	Traditionally latent class (LC) analysis is used by applied researchers as a tool for identifying substantively meaningful clusters. More recently, LC models have also been used as a density estimation tool for categorical variables. We introduce a divisive LC (DLC) model as a density estimation tool that may offer several advantages in comparison to a standard LC model. When using an LC model for density estimation, a considerable number of increasingly large LC models may have to be estimated before sufficient model-fit is achieved. A DLC model consists of a sequence of small LC models. Therefore, a DLC model can be estimated much faster and can easily utilize multiple processor cores, meaning that this model is more widely applicable and practical. In this study we describe the algorithm of fitting a DLC model, and discuss the various settings that indirectly influence the precision of a DLC model as a density estimation tool. These settings are illustrated using a synthetic data example, and the best performing algorithm is applied to a real-data example. The generated data example showed that, using specific decision rules, a DLC model is able to correctly model complex associations amongst categorical variables.
Starting Page	52
Ending Page	72
Page Count	21
File Format	PDF
ISSN	01764268
Journal	Journal of Classification
Volume Number	33
Issue Number	1
e-ISSN	14321343
Language	English
Publisher	Springer US
Publisher Date	2016-02-23
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Latent class analysis Categorical data Mixture model Density estimation Divisive latent class model Missing data Multiple imputation. Statistical Theory and Methods Pattern Recognition Bioinformatics Signal, Image and Speech Processing Psychometrics Marketing
Content Type	Text
Resource Type	Article
Subject	Mathematics Psychology Library and Information Sciences Statistics, Probability and Uncertainty

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis

On the Imputation of Missing Data in Surveys with Likert-Type Scales

Model-Based Clustering for Conditionally Correlated Categorical Data

Fitting a Mixture Model to Three-Mode Three-Way Data with Categorical and Continuous Variables

Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis

Incremental Tree-Based Missing Data Imputation with Lexicographic Ordering

Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm

Constrained Multilevel Latent Class Models for the Analysis of Three-Way Three-Mode Binary Data

Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering

Divisive Latent Class Modeling as a Density Estimation Method for Categorical Data

Similar Documents

Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis

On the Imputation of Missing Data in Surveys with Likert-Type Scales

Model-Based Clustering for Conditionally Correlated Categorical Data

Fitting a Mixture Model to Three-Mode Three-Way Data with Categorical and Continuous Variables

Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis

Incremental Tree-Based Missing Data Imputation with Lexicographic Ordering

Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm

Constrained Multilevel Latent Class Models for the Analysis of Three-Way Three-Mode Binary Data

Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering

Divisive Latent Class Modeling as a Density Estimation Method for Categorical Data