A Latent Block Distance-Association Model for Profile by Profile Cross-Classified Categorical Data
Bibliographic Data
| ID | 19289863 |
|---|---|
| Authors | J Fernando Vera (0000-0002-6499-7132, Universidad de Granada, corresponding author), Mark de Rooij (0000-0001-7308-6210, Leiden University) |
| Year | 2020 |
| Volume | 55 |
| Issue | 3 |
| Pages | 329-343 |
| Publication date | 2020-05-03 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2019.1634995 |
| PMID | 31352798 |
| OpenAlex | W2966349236 |
| Language | EN |
| References cited | 34 |
Distance association models constitute a useful tool for the analysis and graphical representation of cross-classified data in which distances between points inversely describe the association between two categorical variables. When the number of cells is large and the data counts result in sparse tables, the combination of clustering and representation reduces the number of parameters to be estimated and facilitates interpretation. In this article, a latent block distance-association model is proposed to apply block clustering to the outcomes of two categorical variables while the cluster centers are represented in a low dimensional space in terms of a distance-association model. This model is particularly useful for contingency tables in which both the rows and the columns are characterized as profiles of sets of response variables. The parameters are estimated under a Poisson sampling scheme using a generalized EM algorithm. The performance of the model is tested in a Monte Carlo experiment, and an empirical data set is analyzed to illustrate the model
Block (permutation group theory) · Categorical variable · Cluster analysis · Contingency table · Data mining · Latent variable · Latent variable model · Pattern recognition (psychology) · Representation (politics) · Statistics · Artificial Intelligence · Computer Science · Mathematics · Sensory Analysis and Statistical Methods · Spatial and Panel Data Analysis · Statistical Methods and Bayesian Inference
Orthogonal Rotation to Congruence
Models for Longitudinal Data
Finite Mixture Models
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Estimating the Dimension of a Model
Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis
Local Optima in Mixture Modeling
Criteria for Determining Whether Certain Categories in a Cross-Classification Table Should Be Combined, with Special Reference to Occupational Categories in an Occupational Mobility Table
| Highly cited | No |
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