Fully Gibbs Sampling Algorithms for Bayesian Variable Selection in Latent Regression Models

Autor/inn/en	Yamaguchi, Kazuhiro; Zhang, Jihong
Titel	Fully Gibbs Sampling Algorithms for Bayesian Variable Selection in Latent Regression Models
Quelle	In: Journal of Educational Measurement, 60 (2023) 2, S.202-234 (33 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Yamaguchi, Kazuhiro) ORCID (Zhang, Jihong)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/jedm.12348
Schlagwörter	Algorithms; Simulation; Mathematics Achievement; Bayesian Statistics; Item Response Theory; Evaluation Methods + Suchen Sie Ihr Suchwort? Algorithm; Algorithmus; Simulation program; Simulationsprogramm; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Item-Response-Theorie
Abstract	This study proposed Gibbs sampling algorithms for variable selection in a latent regression model under a unidimensional two-parameter logistic item response theory model. Three types of shrinkage priors were employed to obtain shrinkage estimates: double-exponential (i.e., Laplace), horseshoe, and horseshoe+ priors. These shrinkage priors were compared to a uniform prior case in both simulation and real data analysis. The simulation study revealed that two types of horseshoe priors had a smaller root mean square errors and shorter 95% credible interval lengths than double-exponential or uniform priors. In addition, the horseshoe+ prior was slightly more stable than the horseshoe prior. The real data example successfully proved the utility of horseshoe and horseshoe+ priors in selecting effective predictive covariates for math achievement. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01