Mixed and Mixture Regression Models for Continuous Bounded Responses Using the Beta Distribution

作者: Jay Verkuilen , Michael Smithson

DOI: 10.3102/1076998610396895

关键词: Regression analysisApplied mathematicsBeta distributionMarkov chain Monte CarloMathematicsStatisticsGeneralized linear modelMixture modelProbability distributionBounded functionNormal distribution

摘要: Doubly bounded continuous data are common in the social and behavioral sciences. Examples include judged probabilities, confidence ratings, derived proportions such as percent time on task, scale scores. Dependent variables of this kind often difficult to analyze using normal theory models because their distributions may be quite poorly modeled by distribution. The authors extend beta-distributed generalized linear model (GLM) proposed Smithson Verkuilen (2006) discrete mixtures beta distributions, which enables modeling dependent structures commonly found real settings. discuss estimation both deterministic marginal maximum likelihood stochastic Markov chain Monte Carlo (MCMC) methods. results illustrated three sets from cognitive psychology experiments.

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