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: | |
| 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 |
| 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. |
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| ISSN: | 0033-3123 |
| DOI: | 10.1007/s11336-010-9168-2 |
