Latent Structure Analysis of a Set of Multidimensional Contingency Tables

Clogg, Clifford C.; Goodman, Leo A.

doi:10.1080/01621459.1984.10477093

Cited by 234 publications

(140 citation statements)

References 27 publications

(22 reference statements)

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“…Standard references include Titterington et al (1985), Bartholomew and Knott (1999), and Wedel and Kamakura (2000). A few researchers in other fields have estimated latent-class models with attitudinal data (Clogg and Goodman, 1984;McCutcheon, 1987;McCutcheon and Nawojcyzk, 1995;De Menezes and Bartholomew, 1996;Yamaguchi, 2000;Eid et al, 2003;Thacher et al, 2003). Applications of latent-class models The approach outlined in this paper assumes that what people do and say are manifestations of underlying stable preferences.…”

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confidence: 99%

Using Angler Characteristics and Attitudinal Data to Identify Environmental Preference Classes: A Latent-Class Model

Morey

Thacher

Breffle

2006

Environ Resource Econ

161

110

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A latent-class model of environmental preference groups is developed and estimated with only the answers to a set of attitudinal questions. Economists do not typically use this type of data in estimation. Group membership is latent/unobserved. The intent is to identify and characterize heterogeneity in the preferences for environmental amenities in terms of a small number of preference groups. The application is to preferences over the fishing characteristics of Green Bay. Anglers answered a number of attitudinal questions, including the importance of boat fees, species catch rates, and fish consumption advisories on site choice. The results suggest that Green Bay anglers separate into a small number of distinct classes with varying preferences and willingness to pay for a PCB-free Green Bay. The probability that an angler belongs to each class is estimated as function of observable characteristics of the individual. Estimation is with the expectation–maximization (E–M) algorithm, a technique new to environmental economics that can be used to do maximum-likelihood estimation with incomplete information. As explained, a latent-class model estimated with attitudinal data can be melded with a latent-class choice model. Copyright Springer 2006attitudinal data, E–M algorithm, latent-class attitudinal model, latent-class joint model,

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mentioning

confidence: 99%

Using Angler Characteristics and Attitudinal Data to Identify Environmental Preference Classes: A Latent-Class Model

Morey

Thacher

Breffle

2006

Environ Resource Econ

161

110

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“…LCA에서는 관측 가능한 여러 변수를 이용하여 능력, 태도, 가치와 같은 직접 관측하기 어려운 변수 를 측정하고 문항들의 응답 패턴을 통해 개체들을 몇 개의 잠재범주(latent class)로 분류하는 통계적 기법이다 (Clogg와 Goodman, 1984;Goodman 1974). 잠재범주를 측정하는 M 개의 문항변수 Y = (Y1, .…”

Section: 공변량을 고려한 잠재범주분석unclassified

Latent causal inference using the propensity score from latent class regression model

Lee¹,

Chung²

2017

Korean Journal of Applied Statistics

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Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. The matching with the propensity score is one of the most popular methods to control the confounders in order to evaluate the effect of the treatment on the outcome variable. Recently, new methods for the causal inference in latent class analysis (LCA) have been proposed to estimate the average causal effect (ACE) of the treatment on the latent discrete variable. They have focused on the application study for the real dataset to estimate the ACE in LCA. In practice, however, the true values of the ACE are not known, and it is difficult to evaluate the performance of the estimated the ACE. In this study, we propose a method to generate a synthetic data using the propensity score in the framework of LCA, where treatment and outcome variables are latent. We then propose a new method for estimating the ACE in LCA and evaluate its performance via simulation studies. Furthermore we present an empirical analysis based on data form the 'National Longitudinal Study of Adolescents Health,' where puberty as a latent treatment and substance use as a latent outcome variable.

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“…A second reason for computing the Bayes factor is that it can be used when comparing non-nested models. This makes the Bayes factor particularly suitable for use in constrained mixture models, where alternative models are non-nested [23]. In order to test the genetic association, we computed the Bayes factor from the Markov chain-Monte Carlo simulation of the posterior distribution.…”

Section: Bayesian Testingmentioning

confidence: 99%

Bayesian Genetic Association Test when Secondary Phenotypes Are Available Only in the Case Group

Kim

Kwak

2016

Entropy

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In many case-control genetic association studies, a secondary phenotype that may have common genetic factors with disease status can be identified. When information on the secondary phenotype is available only for the case group due to cost and different data sources, a fitting linear regression model ignoring supplementary phenotype data may provide limited knowledge regarding genetic association. We set up a joint model and use a Bayesian framework to estimate and test the effect of genetic covariates on disease status considering the secondary phenotype as an instrumental variable. The application of our proposed procedure is demonstrated through the rheumatoid arthritis data provided by the 16th Genetic Analysis Workshop.

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Latent Structure Analysis of a Set of Multidimensional Contingency Tables

Cited by 234 publications

References 27 publications

Using Angler Characteristics and Attitudinal Data to Identify Environmental Preference Classes: A Latent-Class Model

Using Angler Characteristics and Attitudinal Data to Identify Environmental Preference Classes: A Latent-Class Model

Latent causal inference using the propensity score from latent class regression model

Bayesian Genetic Association Test when Secondary Phenotypes Are Available Only in the Case Group

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