Latent regression analysis
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Publication:4970576
DOI10.1177/1471082X0801000202WikidataQ33962271 ScholiaQ33962271MaRDI QIDQ4970576
Publication date: 7 October 2020
Published in: Statistical Modelling (Search for Journal in Brave)
EM algorithmskew-normal distributionbeta distributionquasi-Newton algorithmsplacebo effectfinite and infinite mixtures
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- Algorithm AS 136: A K-Means Clustering Algorithm
- The use of mixtures for dealing with non-normal regression errors
- On three and five parameter bivariate beta distributions
- Identifiability of continuous mixtures of unknown Gaussian distributions
- A bivariate beta distribution.
- Dirichlet generalizations of latent-class models
- A mixture likelihood approach for generalized linear models
- Generalized latent trait models
- Mixture Models for Estimating the Size of a Closed Population When Capture Rates Vary among Individuals
- Latent class regression on latent factors
- Logistic-Normal Distributions: Some Properties and Uses
- Statistical Applications of the Multivariate Skew Normal Distribution
- Latent Variable Regression for Multiple Discrete Outcomes
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- Clustering for Sparsely Sampled Functional Data
- 10.1162/jmlr.2003.3.4-5.993
- Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
- Allometric Extension
- Exploratory latent structure analysis using both identifiable and unidentifiable models
- A Limited Memory Algorithm for Bound Constrained Optimization
- An analysis of variance test for normality (complete samples)
- Estimating the components of a mixture of normal distributions
- The elements of statistical learning. Data mining, inference, and prediction
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