Modeling Noisy Data with Differential Equations Using Observed and Expected Matrices

Autor/inn/en	Deboeck, Pascal R.; Boker, Steven M.
Titel	Modeling Noisy Data with Differential Equations Using Observed and Expected Matrices
Quelle	In: Psychometrika, 75 (2010) 3, S.420-437 (18 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-010-9168-2
Schlagwörter	Psychometrics; Models; Statistical Analysis; Measurement; Measurement Techniques; Data; Equations (Mathematics); Calculus + Suchen Sie Ihr Suchwort? Psychometry; Psychometrie; Analogiemodell; Statistische Analyse; Messverfahren; Messtechnik; Daten; Equations; Mathematics; Gleichungslehre; Analysis; Differenzialrechnung; Infinitesimalrechnung; Integralrechnung
Abstract	Complex intraindividual variability observed in psychology may be well described using differential equations. It is difficult, however, to apply differential equation models in psychological contexts, as time series are frequently short, poorly sampled, and have large proportions of measurement and dynamic error. Furthermore, current methods for differential equation modeling usually consider data that are atypical of many psychological applications. Using embedded and observed data matrices, a statistical approach to differential equation modeling is presented. This approach appears robust to many characteristics common to psychological time series. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10