Modeling Unobserved Heterogeneity in New York Dairy Farms: One-Stage versus Two-Stage 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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Bayesian nonparametric latent class model for longitudinal data

Statistical Methods in Medical Research ◽

10.1177/0962280220928384 ◽

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.

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Examining Class Separation Contribution by Observed Indicators in Latent Class Models: A Multiple Testing Procedure

Structural Equation Modeling A Multidisciplinary Journal ◽

10.1080/10705511.2018.1554446 ◽

2019 ◽

Vol 27 (1) ◽

pp. 88-96

Author(s):

Tenko Raykov ◽

Michael Harrison ◽

George A. Marcoulides

Keyword(s):

Multiple Testing ◽

Latent Class ◽

Testing Procedure ◽

Latent Class Models ◽

Multiple Testing Procedure ◽

Class Separation ◽

Class Models

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence: Navigating the absence of a gold standard

PLoS ONE ◽

10.1371/journal.pone.0257743 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0257743

Author(s):

Sahar Saeed ◽

Sheila F. O’Brien ◽

Kento Abe ◽

Qi-Long Yi ◽

Bhavisha Rathod ◽

...

Keyword(s):

Severe Acute Respiratory Syndrome ◽

Gold Standard ◽

Latent Class ◽

Latent Class Model ◽

Credible Interval ◽

Latent Class Models ◽

Igg Antibody ◽

Cross Sectional ◽

Class Models ◽

Over Time

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence studies bridge the gap left from case detection, to estimate the true burden of the COVID-19 pandemic. While multiple anti-SARS-CoV-2 immunoassays are available, no gold standard exists. Methods This serial cross-sectional study was conducted using plasma samples from 8999 healthy blood donors between April-September 2020. Each sample was tested by four assays: Abbott SARS-Cov-2 IgG assay, targeting nucleocapsid (Abbott-NP) and three in-house IgG ELISA assays (targeting spike glycoprotein, receptor binding domain, and nucleocapsid). Seroprevalence rates were compared using multiple composite reference standards and by a series of Bayesian Latent Class Models. Result We found 13 unique diagnostic phenotypes; only 32 samples (0.4%) were positive by all assays. None of the individual assays resulted in seroprevalence increasing monotonically over time. In contrast, by using the results from all assays, the Bayesian Latent Class Model with informative priors predicted seroprevalence increased from 0.7% (95% credible interval (95% CrI); 0.4, 1.0%) in April/May to 0.7% (95% CrI 0.5, 1.1%) in June/July to 0.9% (95% CrI 0.5, 1.3) in August/September. Assay characteristics varied over time. Overall Spike had the highest sensitivity (93.5% (95% CrI 88.7, 97.3%), while the sensitivity of the Abbott-NP assay waned from 77.3% (95% CrI 58.7, 92.5%) in April/May to 64.4% (95% CrI 45.6, 83.0) by August/September. Discussion Our results confirmed very low seroprevalence after the first wave in Canada. Given the dynamic nature of this pandemic, Bayesian Latent Class Models can be used to correct for imperfect test characteristics and waning IgG antibody signals.

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About the Connection Between Knowledge Structures and Latent Class Models

Methodology ◽

10.1027/1614-2241.1.3.93 ◽

2005 ◽

Vol 1 (3) ◽

pp. 93-103 ◽

Cited By ~ 15

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.

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Recommendations on the Sample Sizes for Multilevel Latent Class Models

Educational and Psychological Measurement ◽

10.1177/0013164417719111 ◽

2017 ◽

Vol 78 (5) ◽

pp. 737-761 ◽

Cited By ~ 9

Author(s):

Jungkyu Park ◽

Hsiu-Ting Yu

Keyword(s):

Latent Class ◽

Latent Class Model ◽

Latent Class Models ◽

Sample Sizes ◽

Nested Structures ◽

Accuracy Parameter ◽

Multilevel Latent Class Model ◽

Error Bias ◽

Design Factors ◽

Class Models

A multilevel latent class model (MLCM) is a useful tool for analyzing data arising from hierarchically nested structures. One important issue for MLCMs is determining the minimum sample sizes needed to obtain reliable and unbiased results. In this simulation study, the sample sizes required for MLCMs were investigated under various conditions. A series of design factors, including sample sizes at two levels, the distinctness and the complexity of the latent structure, and the number of indicators were manipulated. The results revealed that larger samples are required when the latent classes are less distinct and more complex with fewer indicators. This study also provides recommendations about the minimum required sample sizes that satisfied all four criteria—model selection accuracy, parameter estimation bias, standard error bias, and coverage rate—as well as rules of thumb for sample size requirements when applying MLCMs in data analysis.

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