Results 291 to 300 of about 548,673 (345)
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Biometrics, 2000
Summary.In many areas of medical research, such as psychiatry and gerontology, latent class variables are used to classify individuals into disease categories, often with the intention of hierarchical modeling. Problems arise when it is not clear how many disease classes are appropriate, creating a need for model selection and diagnostic techniques ...
Garrett, Elizabeth S., Zeger, Scott L.
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Summary.In many areas of medical research, such as psychiatry and gerontology, latent class variables are used to classify individuals into disease categories, often with the intention of hierarchical modeling. Problems arise when it is not clear how many disease classes are appropriate, creating a need for model selection and diagnostic techniques ...
Garrett, Elizabeth S., Zeger, Scott L.
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Advances in Data Analysis and Classification, 2013
The paper proposes a latent class version of Combination of Uniform and (shifted) Binomial random variables ( CUB ) models for ordinal data to account for unobserved heterogeneity. The extension, called LC-CUB , is useful when the heterogeneity is originated by clusters of respondents not identified by covariates: this may generate a multimodal ...
Leonardo Grilli +3 more
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The paper proposes a latent class version of Combination of Uniform and (shifted) Binomial random variables ( CUB ) models for ordinal data to account for unobserved heterogeneity. The extension, called LC-CUB , is useful when the heterogeneity is originated by clusters of respondents not identified by covariates: this may generate a multimodal ...
Leonardo Grilli +3 more
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Dirichlet Generalizations of Latent-Class Models
Journal of Classification, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Richard F. Potthoff +2 more
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2010
A statistical model can be called a latent class (LC) or mixture model if it assumes that some of its parameters differ across unobserved subgroups, LCs, or mixture components. This rather general idea has several seemingly unrelated applications, the most important of which are clustering, scaling, density estimation, and random-effects modeling. This
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A statistical model can be called a latent class (LC) or mixture model if it assumes that some of its parameters differ across unobserved subgroups, LCs, or mixture components. This rather general idea has several seemingly unrelated applications, the most important of which are clustering, scaling, density estimation, and random-effects modeling. This
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A Sparse Latent Class Model for Cognitive Diagnosis
Psychometrika, 2020Cognitive diagnostic models (CDMs) are latent variable models developed to infer latent skills, knowledge, or personalities that underlie responses to educational, psychological, and social science tests and measures. Recent research focused on theory and methods for using sparse latent class models (SLCMs) in an exploratory fashion to infer the latent
Yinyin Chen +2 more
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The latent class multitrait-multimethod model.
Psychological Methods, 2015A latent class multitrait-multimethod (MTMM) model is proposed to estimate random and systematic measurement error in categorical survey questions while making fewer assumptions than have been made so far in such evaluations, allowing for possible extreme response behavior and other nonmonotone effects.
Oberski, D.L. +2 more
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Bootstrapping Latent Class Models
2005This paper deals with improved measures of statistical accuracy for parameter estimates of latent class models. It introduces more precise confidence intervals for the parameters of this model, based on parametric and nonparametric bootstrap. Moreover, the label-switching problem is discussed and a solution to handle it introduced.
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Multivariate Behavioral Research, 2014
The Multilevel Latent Class Model (MLCM) proposed by Vermunt (2003) has been shown to be an excellent framework for analyzing nested data with assumed discrete latent constructs. The nonparametric version of MLCM assumes 2 levels of discrete latent components to describe the dependency observed in data.
Hsiu-Ting, Yu, Jungkyu, Park
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The Multilevel Latent Class Model (MLCM) proposed by Vermunt (2003) has been shown to be an excellent framework for analyzing nested data with assumed discrete latent constructs. The nonparametric version of MLCM assumes 2 levels of discrete latent components to describe the dependency observed in data.
Hsiu-Ting, Yu, Jungkyu, Park
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On identifiability of certain latent class models
Statistics & Probability Letters, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Latent Class Models on Business Analytics
2019 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD), 2019This paper discusses the systematic application of the latent class model on business analytics. The latent class model is one of the effective statistical model classes on business analytics to represent essential statistical structures by learning the sparse and high dimensional data. This model class is useful for the purpose of reduction of feature
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