Modeling Noisy Data with Differential Equations Using Observed and Expected Matrices

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Title: Modeling Noisy Data with Differential Equations Using Observed and Expected Matrices
Language: English
Authors: Deboeck, Pascal R., Boker, Steven M.
Source: Psychometrika. Sep 2010 75(3):420-437.
Availability: Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
Peer Reviewed: Y
Physical Description: PDF
Page Count: 18
Publication Date: 2010
Document Type: Journal Articles
Reports - Descriptive
Descriptors: Psychometrics, Models, Statistical Analysis, Measurement, Measurement Techniques, Data, Equations (Mathematics), Calculus
DOI: 10.1007/s11336-010-9168-2
ISSN: 0033-3123
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.
Abstractor: As Provided
Number of References: 17
Entry Date: 2010
Accession Number: EJ895493
Database: ERIC
FullText Text:
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  Data: 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.
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