Two Filtering Methods of Forecasting Linear and Nonlinear Dynamics of Intensive Longitudinal Data
Author:
Funder
National Institute on Drug Abuse
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,General Psychology
Link
https://link.springer.com/content/pdf/10.1007/s11336-021-09827-5.pdf
Reference91 articles.
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2. Anderson, B. D. O., & Moore, J. B. (1979). Optimal filtering. Prentice-Hall.
3. Anderson, J. L. (1996). A method for producing and evaluating probabilistic forecasts from ensemble model integrations. Journal of Climate, 9(7), 1518–1530. 10.1175/1520-0442(1996)009$$<$$1518:amfpae$$>$$2.0.co;2
4. Anderson, J. L. (2001). An ensemble adjustment Kalman filter for data assimilation. Monthly Weather Review, 129(12), 2884–2903. 10.1175/1520-0493(2001)129$$<$$2884:aeakff$$>$$2.0.co;2
5. Anderson, J. L., & Anderson, S. L. (1999). A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Monthly Weather Review, 127(12), 2741–2758. 10.1175/1520-0493(1999)127$$<$$2741:amciot$$>$$2.0.co;2
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2. Erratum to: Two Filtering Methods of Forecasting Linear and Nonlinear Dynamics of Intensive Longitudinal Data;Psychometrika;2022-04-13
3. Guest Editors’ Introduction to the Special Issue on Forecasting with Intensive Longitudinal Data;Psychometrika;2022-03-01
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