Estimation of the Nonlinear Random Coefficient Model when Some Random Effects Are Separable

Autor/inn/en	du Toit, Stephen H. C.; Cudeck, Robert
Titel	Estimation of the Nonlinear Random Coefficient Model when Some Random Effects Are Separable
Quelle	In: Psychometrika, 74 (2009) 1, S.65-82 (18 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-008-9107-7
Schlagwörter	Computation; Models; Measurement Techniques; Research Methodology; Evaluation Methods; Psychometrics; Regression (Statistics); Maximum Likelihood Statistics + Suchen Sie Ihr Suchwort? Analogiemodell; Messtechnik; Research method; Forschungsmethode; Psychometry; Psychometrie; Regression; Regressionsanalyse
Abstract	A method is presented for marginal maximum likelihood estimation of the nonlinear random coefficient model when the response function has some linear parameters. This is done by writing the marginal distribution of the repeated measures as a conditional distribution of the response given the nonlinear random effects. The resulting distribution then requires an integral equation that is of dimension equal to the number of nonlinear terms. For nonlinear functions that have linear coefficients, the improvement in computational speed and accuracy using the new algorithm can be dramatic. An illustration of the method with repeated measures data from a learning experiment is presented. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10