Bayesian Extension of Biweight and Huber Weight for Robust Ability Estimation

Autor/inn/en	Maeda, Hotaka; Zhang, Bo
Titel	Bayesian Extension of Biweight and Huber Weight for Robust Ability Estimation
Quelle	In: Journal of Educational Measurement, 57 (2020) 1, S.51-70 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/jedm.12240
Schlagwörter	Bayesian Statistics; Robustness (Statistics); Computation; Monte Carlo Methods + Suchen Sie Ihr Suchwort? Widerstandsfähigkeit; Monte-Carlo-Methode
Abstract	When a response pattern does not fit a selected measurement model, one may resort to robust ability estimation. Two popular robust methods are biweight and Huber weight. So far, research on these methods has been quite limited. This article proposes the maximum a posteriori biweight (BMAP) and Huber weight (HMAP) estimation methods. These methods use the Bayesian prior distribution to compensate for information lost due to aberrant responses. They may also be more resistant to the detrimental effects of downweighting the nonaberrant responses. The effectiveness of BMAP and HMAP was evaluated through a Monte Carlo simulation. Results show that both methods, especially BMAP, are more effective than the original biweight and Huber weight in correcting mild forms of aberrant behavior. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01