A bivariate finite mixture growth model with selection

DOI10.1007/s11634-020-00433-4OpenAlexW3116374330MaRDI QIDQ2051585

David Aristei, Francesco Bartolucci, Silvia Bacci, Silvia Pandolfi

Publication date: 24 November 2021

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-020-00433-4

zbMATH Keywords

endogeneitylongitudinal datalatent class modelselection modelhousehold portfolio choiceslatent trajectories

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Uses Software

• bootstrap

Cites Work

• A bootstrap-based aggregate classifier for model-based clustering
• Estimating the dimension of a model
• A comparison of some criteria for states selection in the latent Markov model for longitudinal data
• Latent Curve Models
• Inference and missing data
• Sample Selection Bias as a Specification Error
• Direct Calculation of the Information Matrix via the EM Algorithm
• Latent Variable Regression for Multiple Discrete Outcomes
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Retracted: Growing up in a Recession
• Computational Statistics
• Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
• A Heckman Selection-tModel
• A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure
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