Using innovative outliers to detect discrete shifts in dynamics in group-based state-space models

Multivariate Behavioral Research

Volumn 44, Issue 4, 2009, Pages 465-496

  Chow, Sy Miin ^a   Hamaker, Ellen L ^b   Allaire, Jason C ^c  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

^b UTRECHT UNIVERSITY   (Netherlands)

^c NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77951662372     PISSN: 00273171     EISSN: None     Source Type: Journal    
DOI: 10.1080/00273170903103324     Document Type: Article

References (65)

1. Allaire, J. C., & Marsiske, M. 2005. Intraindividual variability may not always indicate vulnerability in elders' cognitive performance. Psychology and Aging, 20, 390-401.
2. Anderson, B. D. O., & Moore, J. B. 1979. Optimal filtering. Englewood Cliffs, NJ: Prentice Hall.
3. Ansley, C. F., & Kohn, R. 1985. Estimation, filtering and smoothing in state space models with incompletely specified initial conditions. Annals of Statistics, 13, 1286-1316.
4. Boker, S. M., Xu, M., Rotondo, J. L., & King, K. 2002. Windowed cross-correlation and peak picking for the analysis of variability in the association between behavioral time series. Psychological Methods, 7(1), 338-355.
5. Bolger, N., Davis, A., & Rafaeli, E. (2003). Dairy methods: Capturing life as it is lived. Annual Review of Psychology, 54, 579-616.
6. Bollen, K. A., & Curran, P. J. (2004). Autoregressive latent trajectory (ALT) models: A synthesis of two traditions. Sociological Methods & Research, 32, 336-383.
7. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307-327.
8. Caines, P. E., & Rissanen, J. (1974). Maximum likelihood estimation of parameters. IEEE Transactions on Information Theory, IT-20, 102-104.
9. Caplan, L. J., & Schooler, C. (2003). The roles of fatalism, self-confidence, and intellectual resources in the disablement process in older adults. Psychology and Aging, 18, 551-561.
10. Carstensen, L. L. (1993). Motivation for social contact across the life span: A theory of socioemotional selectivity. In J. E. Jacobs (Ed.), Nebraska Symposium on Motivation (pp. 209-254). Lincoln: University of Nebraska Press.
11. Chow, S.-M., Ferrer, E., & Nesselroade, J. R. (2007). An unscented Kalman filter approach to the estimation of nonlinear dynamical systems models. Multivariate Behavioral Research, 42(2), 283-321.
12. Chow, S.-M., & Filteau, G. (2009). A state-space approach to representing discontinuous shifts in change processes. In K. M. Newell & P. C. M. Molenaar (Eds.), Individual pathways of change in learning and development. Washington, DC: American Psychological Association.
13. Chow, S.-M., Hamagami, F., & Nesselroade, J. R. (2007). Age differences in dynamical cognition-emotion linkages. Psychology and Aging, 22(4), 765-780.
14. Chow, S.-M., Ho, M.-H. R., Hamaker, E. J., & Dolan, C. V. (in press). Equivalences and differences between structural equation and state-space modeling frameworks. Structural Equation Modeling.
15. Chow, S.-M., & Zhang, G. (2008). Continuous-time modeling of irregularly spaced panel data using a cubic spline interpolation technique (special issue on continuous-time modeling of panel data coedited by J. H. L. Oud & H. Singer). Statistica Neerlandica, 62(1), 131-154.
16. Curran, P. J. (2003). Have multilevel models been structural equation models all along? Multivariate Behavioral Research, 38, 529-569.
17. De Jong, P. (1988). A cross-validation filter for time series models. Biometrika, 75, 594-600.
18. De Jong, P. (1989). Smoothing and interpolation with the state-space model. Journal of the American Statistical Association, 84(408), 1085-1088.
19. De Jong, P. (1991). The diffuse Kalman filter. The Annals of Statistics, 19(2), 1073-1083.
20. De Jong, P., & Lin, S. C.-C. (2003). Smoothing with an unknown initial condition. Journal of Time Series Analysis, 24(2), 141-148.
21. De Jong, P., & Penzer, J. (1998). Diagnosing shocks in time series. Journal of American Statistical Association, 93, 796-806.
22. Delsing, M., & Oud, J. (2008). Analyzing reciprocal relationships by means of the continuous-time autoregressive latent trajectory model. Statistica Neerlandica, 62, 58-82.
23. Diener, E., Fujita, F., & Smith, H. (1995). The personality structure of affect. Journal of Personality and Social Psychology, 69(1), 130-141.
24. Doornik, J. A. (1998). Object-oriented matrix programming using Ox 2.0. London: Timberlake Consultants Press.
25. Durbin, J., & Koopman, S. J. (2001). Time series analysis by state-space methods. New York: Oxford University Press.
26. Eizenman, D. R., Nesselroade, J. R., Featherman, D. L., & Rowe, J. W. (1997). Intra-individual variability in perceived control in an elderly sample: The MacArthur Successful Aging Studies. Psychology and Aging, 12, 489-502.
27. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987-1008.
28. Fahrmeir, L., & Tutz, G. (1994). Multivariate statistical modelling based on generalized linear models. Berlin, Germany: Springer-Verlag.
29. Fox, A. J. (1972). Outliers in time series. Journal of the Royal Statistical Society, Series B, 34, 350-363.
30. Gamaldo, A., Weatherbee, S., & Allaire, J. (2008). Exploring the within-person coupling of blood pressure and cognition in the elderly. Journal of Gerontology: Psychological Science, 63, 386-389.
31. Gilmore, R. (1981). Catastrophe theory for scientists and engineers. New York: Dover.
32. Haken, H., Kelso, J. A. S., & Bunz, H. (1985). A theoretical model of phase transitions in human hand movements. Biological Cybernetics, 51, 347-356.
33. Harvey, A. C. (2001). Forecasting, structural time series models and the Kalman filter. Cambridge, UK: Cambridge University Press.
34. Harvey, A. C., & Koopman, S. J. (1992). Diagnostic checking of unobservedcomponents time series models. Journal of Business and Economic Statistics, 10, 377-389.
35. Jones, R. H. (1993). Longitudinal data with serial correlation: A state-space approach. Boca Raton, FL: Chapman & Hall/CRC.
36. Kitagawa, G. (1998). A self-organizing state-space model. Journal of the American Statistical Association, 93(443), 1203-1215.
37. Kohn, R., & Ansley, C. F. (1987). A new algorithm for spline smoothing based on smoothing a stochastic process. SIAM Journal of Scientific and Statistical Computing, 8, 33-48.
38. Kohn, R., & Ansley, C. F. (1989). A fast algorithm for signal extraction, influence and cross-validation. Biometrika, 76, 65-79.
39. Koopman, S. J. (1997). Exact initial Kalman filter and smoother for non-stationary time series models. Journal of the American Statistical Association, 92, 1630-1638.
40. Koopman, S. J., Shephard, N., & Doornik, J. A. (1999). Statistical algorithms for models in state space using ssfpack 2.2. Econometrics Journal, 2, 113-166.
41. Krause, N., & Shaw, B. A. (2003). Role-specific control, personal meaning, and health in late life. Research on Aging, 25, 559-586.
42. Lachman, M. E. (2006). Perceived control over aging-related declines: Adaptive beliefs and behaviors. Current Directions in Psychological Science, 15(6), 282-286.
43. MacCallum, R. C., & Ashby, G. F. (1986). Relationships between linear systems theory and covariance structure modeling. Journal of Mathematical Psychology, 30(1), 1-27.
44. McArdle, J. J., & Hamagami, F. (2001). Latent difference score structural models for linear dynamic analysis with incomplete longitudinal data. In L. Collins & A. Sayer (Eds.), New methods for the analysis of change (pp. 139-175). Washington, DC: American Psychological Association.
45. Molenaar, P. C. M., & Newell, K. M. (2003). Direct fit of a theoretical model of phase transition in oscillatory finger motions. British Journal of Mathematical and Statistical Psychology, 56, 199-214.
46. Neupert, S., Almeida, D., Mroczek, D., & Spiro, A. I. (2006). Daily stressors and memory failures in a naturalistic setting: Findings from the VA Normative Aging Study. Psychology and Aging, 21,424-429.
47. Ord, J. K., Koehler, A., & Snyder, R. (1997). Estimation and prediction for a class of dynamic nonlinear statistical models. Journal of the American Statistical Association, 92, 1621-1629.
48. Otter, P. (1986). Dynamic structure systems under indirect observation: Indentifiability and estimation aspects from a system theoretic perspective. Psychometrika, 51(3), 415-428.
49. Oud, J. H. L., & Jansen, R. A. R. G. (2000). Continuous time state space modeling of panel data by means of SEM. Psychometrika, 65(2), 199-215.
50. Oud, J. H. L., Jansen, R. A. R. G., van Leeuwe, J. F. L., Aarnoutse, C. A. J., & Voeten, M. J. M. (1999). Monitoring pupil development by means of the Kalman filter and smoother based upon SEM state space modeling. Learning and Individual Differences, 11, 121-136.
51. Oud, J. H. L., van den Bercken, J. H., & Essers, R. J. (1990). Longitudinal factor score estimation using the Kalman filter. Applied Psychological Measurement, 14(4), 395-418.
52. Pearlman, J. G. (1980). An algorithm for the exact likelihood of a high-order autoregressive-moving average process. Biometrika, 67(1), 232-233.
53. Penzer, J. (2007). State space models for time series with patches of unusual observations. Journal of Time Series Analysis, 28(5), 629-645.
54. Piaget, J. (1960). The general problems of the psychobiological development of the child, Volume 4. In J. M. Tanner & B. Inhelder (Eds.), Discussions on child development (pp. 3-28). London: Tavistock.
55. Piaget, J., & Inhelder, B. (1969). The psychology of the child. New York: Basic Books.
56. Schweppe, F. (1965). Evaluation of likelihood functions for gaussian signals. IEEE Transactions on Information Theory, 11, 61-70.
57. Singer, H. (1998). Continuous panel models with time dependent parameters. Journal of Mathematical Sociology, 23(2), 77-98.
58. Tarvainen, M. P., Georgiadis, S. D., Ranta-aho, P. O., & Karjalainen, P. A. (2006). Time-varying analysis of heart rate variability signals with Kalman smoother algorithm. Physiological Measurement, 27(3), 225-239.
59. Thom, R. (1993). Structural stability and morphogenesis: An outline of a general theory of models. Reading, MA: Addison-Wesley.
60. Thurstone, L. L. (1962). Primary mental abilities. Chicago: Science Research Associates.
61. Tsay, R. S. (1988). Outliers, level shifts and variances changes in time series. Journal of Forecasting, 7,1-20.
62. Van der Maas, H. L. J., & Molenaar, P. C. M. (1992). Stagewise cognitive development: An application of catastrophe theory. Psychological Review, 99(3), 395-417.
63. Wagenmakers, E. J., Molenaar, P. C. M., Grasman, R. P. P. P., Hartelman, P. A. I., & van der Maas, H. L. J. (2005). Transformation invariant stochastic catastrophe theory. Physica D, 211, 263-276.
64. West, M., & Harrison, J. (1989). Bayesian forecasting and dynamic models. Berlin, Germany: Springer-Verlag.
65. Whitbourne, S. B., Neupert, N., & Lachman, M. (2009). Daily physical activity: Relation to everyday memory in adulthood. Journal of Applied Gerontology, 27, 331-349.