Bayesian nonparametric latent class model for longitudinal data

2020 ◽  
Vol 29 (11) ◽  
pp. 3381-3395
Author(s):  
Wonmo Koo ◽  
Heeyoung Kim
Keyword(s):  
Longitudinal Data ◽  
Latent Class ◽  
Latent Class Model ◽  
Latent Class Models ◽  
Model Parameters ◽  
Latent Classes ◽  
Bayesian Nonparametric ◽  
Class Model ◽  
Proposed Model ◽  
Class Models

Latent class models have been widely used in longitudinal studies to uncover unobserved heterogeneity in a population and find the characteristics of the latent classes simultaneously using the class allocation probabilities dependent on predictors. However, previous latent class models for longitudinal data suffer from uncertainty in the choice of the number of latent classes. In this study, we propose a Bayesian nonparametric latent class model for longitudinal data, which allows the number of latent classes to be inferred from the data. The proposed model is an infinite mixture model with predictor-dependent class allocation probabilities; an individual longitudinal trajectory is described by the class-specific linear mixed effects model. The model parameters are estimated using Markov chain Monte Carlo methods. The proposed model is validated using a simulated example and a real-data example for characterizing latent classes of estradiol trajectories over the menopausal transition using data from the Study of Women’s Health Across the Nation.

About the Connection Between Knowledge Structures and Latent Class Models

Methodology ◽  
2005 ◽  
Vol 1 (3) ◽  
pp. 93-103 ◽  
Author(s):  
Martin Schrepp
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Knowledge Structure ◽  
Latent Class Models ◽  
Knowledge Structures ◽  
Structure Theory ◽  
Response Patterns ◽  
Class Model ◽  
Exploratory Data ◽  
Class Models

This paper tries to establish a connection between knowledge structures and latent class models. We will show that knowledge structures can be interpreted as a special type of constrained latent class model. Latent class models offer a well-founded theoretical framework to investigate the connection of a given latent class model to observed data. If we establish a connection between latent class models and knowledge structures, we can also use this framework in knowledge structure theory. We will show that the connection to latent class models offers us a possibility to construct a knowledge structure by exploratory data analysis from observed response patterns. Other possible applications are the empirical comparison of hypothetical knowledge structures and the statistical test of a given knowledge structure.

Estimation and Use of Standard Errors of Latent Class Model Parameters

1987 ◽  
Vol 24 (3) ◽  
pp. 298-304
Author(s):  
Rajiv Grover
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Latent Class Models ◽  
Standard Errors ◽  
Model Parameters ◽  
Iterative Proportional Fitting ◽  
Class Model ◽  
Fitting Algorithm ◽  
Marketing Data ◽  
Parsimonious Models

Only recently have latent class models been used effectively to analyze marketing data, though they have been popular for more than a decade in the social sciences. Most research reported in the literture does not include the standard errors of the estimates of the latent class model parameters. The author argues for the usefulness of standard errors while exploring for parsimonious models. He provides an approach to estimating standard errors of all parameters as estimated by the iterative proportional fitting algorithm of Goodman implemented in MLLSA.

scholarly journals Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data

2020 ◽  
Vol 8 (3) ◽  
pp. 30 ◽  
Author(s):  
Alexander Robitzsch
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Simulated Data ◽  
Latent Class Models ◽  
Latent Classes ◽  
Polytomous Item ◽  
Item Functioning ◽  
Item Responses ◽  
Polytomous Item Responses ◽  
Raven's Standard Progressive Matrices

The last series of Raven’s standard progressive matrices (SPM-LS) test was studied with respect to its psychometric properties in a series of recent papers. In this paper, the SPM-LS dataset is analyzed with regularized latent class models (RLCMs). For dichotomous item response data, an alternative estimation approach based on fused regularization for RLCMs is proposed. For polytomous item responses, different alternative fused regularization penalties are presented. The usefulness of the proposed methods is demonstrated in a simulated data illustration and for the SPM-LS dataset. For the SPM-LS dataset, it turned out the regularized latent class model resulted in five partially ordered latent classes. In total, three out of five latent classes are ordered for all items. For the remaining two classes, violations for two and three items were found, respectively, which can be interpreted as a kind of latent differential item functioning.

Mastery Assessment with Latent Class and Quasi-Independence Models Representing Homogeneous Item Domains

1980 ◽  
Vol 5 (1) ◽  
pp. 65-81 ◽  
Author(s):  
John R. Bergan ◽  
Anthony A. Cancelli ◽  
John W. Luiten
Keyword(s):  
Latent Class ◽  
Decision Rules ◽  
Latent Class Models ◽  
Latent Classes ◽  
Classification Techniques ◽  
Complex Models ◽  
Class Models

This article discusses mastery classification involving the use of latent class and quasi-independence models. Extensions of mastery classification techniques developed by Macready and Dayton are presented. These extensions provide decision rules for assigning individuals to latent classes in complex models involving more than two latent categories. Procedures for identifying the minimally acceptable proportion of misclassified individuals in complex latent class models are also detailed.

scholarly journals Joint latent class model for longitudinal data and interval‐censored semi‐competing events: Application to dementia

Biometrics ◽  
2016 ◽  
Vol 72 (4) ◽  
pp. 1123-1135 ◽  
Author(s):  
Anaïs Rouanet ◽  
Pierre Joly ◽  
Jean‐François Dartigues ◽  
Cécile Proust‐Lima ◽  
Hélène Jacqmin‐Gadda
Keyword(s):  
Longitudinal Data ◽  
Latent Class ◽  
Latent Class Model ◽  
Class Model ◽  
Interval Censored ◽  
Competing Events

Reliable Inference in Highly Stratified Contingency Tables: Using Latent Class Models as Density Estimators

2011 ◽  
Vol 19 (2) ◽  
pp. 173-187 ◽  
Author(s):  
Drew A. Linzer
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Contingency Tables ◽  
Latent Class Models ◽  
Small Samples ◽  
Categorical Variables ◽  
Density Estimator ◽  
Class Model ◽  
Wide Range ◽  
Demographic Subgroups

Contingency tables are among the most basic and useful techniques available for analyzing categorical data, but they produce highly imprecise estimates in small samples or for population subgroups that arise following repeated stratification. I demonstrate that preprocessing an observed set of categorical variables using a latent class model can greatly improve the quality of table-based inferences. As a density estimator, the latent class model closely approximates the underlying joint distribution of the variables of interest, which enables reliable estimation of conditional probabilities and marginal effects, even among subgroups containing fewer than 40 observations. Though here focused on applications to public opinion, the procedure has a wide range of potential uses. I illustrate the benefits of the latent class model—based approach for greatly improved accuracy in estimating and forecasting vote preferences within small demographic subgroups using survey data from the 2004 and 2008 U.S. presidential election campaigns.

scholarly journals Modeling Unobserved Heterogeneity in New York Dairy Farms: One-Stage versus Two-Stage Models

2012 ◽  
Vol 41 (3) ◽  
pp. 275-285 ◽  
Author(s):  
Antonio Alvarez ◽  
Julio del Corral ◽  
Loren W. Tauer
Keyword(s):  
New York ◽  
Latent Class ◽  
Unobserved Heterogeneity ◽  
Latent Class Model ◽  
A Priori ◽  
Dairy Farm ◽  
Latent Class Models ◽  
Class Separation ◽  
Heterogeneous Technologies ◽  
Class Models

Agricultural production estimates have often differentiated and estimated different technologies within a sample of farms. The common approach is to use observable farm characteristics to split the sample into groups and subsequently estimate different functions for each group. Alternatively, unique technologies can be determined by econometric procedures such as latent class models. This paper compares the results of a latent class model with the use of a priori information to split the sample using dairy farm data. Latent class separation appears to be a superior method of separating heterogeneous technologies and suggests that technology differences are multifaceted.

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