Bayesian Adaptive Lasso for Ordinal Regression with Latent Variables

Autor/inn/en	Feng, Xiang-Nan; Wu, Hao-Tian; Song, Xin-Yuan
Titel	Bayesian Adaptive Lasso for Ordinal Regression with Latent Variables
Quelle	In: Sociological Methods & Research, 46 (2017) 4, S.926-953 (28 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124115610349
Schlagwörter	Bayesian Statistics; Regression (Statistics); Models; Observation; Correlation; Factor Analysis; Markov Processes; Monte Carlo Methods; Psychological Patterns; Social Science Research + Suchen Sie Ihr Suchwort? Regression; Regressionsanalyse; Analogiemodell; Beobachtung; Korrelation; Faktorenanalyse; Markowscher Prozess; Monte-Carlo-Methode; Social scientific research; Sozialwissenschaftliche Forschung
Abstract	We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct simultaneous estimation and variable selection. Nice features including empirical performance of the proposed methodology are demonstrated by simulation studies. The model is applied to a study on happiness and its potential determinants from the Inter-university Consortium for Political and Social Research. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01