Guest Editors’ Introduction to the Special Issue on Forecasting with Intensive Longitudinal Data
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,General Psychology
Link
https://link.springer.com/content/pdf/10.1007/s11336-022-09850-0.pdf
Reference7 articles.
1. Chow, S-M., Lee, J., Hofman, A. D., van der Maas, H. L. J., Pearl, D. K., & Molenaar, P. C. M. (2022). Control theory forecasts of optimal training dosage to facilitate children’s arithmetic learning in a digital education application. Psychometrika. https://doi.org/10.1007/s11336-021-09829-3.
2. Fisher, Z. F., Kim, Y., Fredrickson, B., & Pipiras, V. (2022). Penalized estimation and forecasting of multiple subject intensive longitudinal data. Psychometrika. https://doi.org/10.1007/s11336-021-09825-7.
3. Hunter, M. D., Fatimah, H., & Bornovalova, M. A. (2022). Two filtering methods of forecasting linear and nonlinear dynamics of intensive longitudinal data. Psychometrika. https://doi.org/10.1007/s11336-021-09827-5.
4. Kelava, A., Kilian, P., Glaesser, J., Merk, S., & Brandt, H. (2022). Forecasting intra-individual changes of affective states taking into account interindividual differences using intensive longitudinal data from a university student drop out study in math. Psychometrika (current issue).
5. Lafit, G., Meers, K., & Ceulemans, E. (2021). A systematic study into the factors that affect the predictive accuracy of multilevel VAR(1) models. Psychometrika. https://doi.org/10.1007/s11336-021-09803-z.
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