Monte Carlo Estimation of the Conditional Rasch Model. Research Report 94-09.

Autor/in	Akkermans, Wies M. W.
Institution	Twente Univ., Enschede (Netherlands). Faculty of Educational Science and Technology.
Titel	Monte Carlo Estimation of the Conditional Rasch Model. Research Report 94-09.
Quelle	(1994), (41 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Estimation (Mathematics); Foreign Countries; Markov Processes; Maximum Likelihood Statistics; Monte Carlo Methods; Statistical Distributions + Suchen Sie Ihr Suchwort? Estimation; Mathematics; Schätzung; Ausland; Markowscher Prozess; Monte-Carlo-Methode; Wahrscheinlichkeitsverteilung
Abstract	In order to obtain conditional maximum likelihood estimates, the so-called conditioning estimates have to be calculated. In this paper a method is examined that does not calculate these constants exactly, but approximates them using Monte Carlo Markov Chains. As an example, the method is applied to the conditional estimation of both item and person parameters in the Rasch model. The key idea for this approach was developed by C. J. Geyer and E. A. Thompson (1992), who showed that, in the exponential family, a quantity that is proportional to the conditioning constant can be expressed as an expectation with respect to a certain distribution. Simulating from this distribution, an estimate of the proportional quantity can be obtained as the observed sample mean. Inserting this estimate into the conditional likelihood then allows one to maximize the approximate likelihood, as the proportionality constant does not depend on the parameters to be estimated. (Contains 5 tables, 1 figure, and 11 references.) (Author/SLD)
Anmerkungen	Bibliotheek, Faculty of Educational Science and Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.
Erfasst von	ERIC (Education Resources Information Center), Washington, DC