-
2
-
-
77951663345
-
Equivalence and differences between structural equation modeling and state-space modeling techniques
-
Chow, S.-M., Ho, M.-H. R., Hamaker, E. L., & Dolan, C. V. (2010). Equivalence and differences between structural equation modeling and state-space modeling techniques. Structural Equation Modeling, 17, 303-332.
-
(2010)
Structural Equation Modeling
, vol.17
, pp. 303-332
-
-
Chow, S.-M.1
Ho, M.-H.R.2
Hamaker, E.L.3
Dolan, C.V.4
-
3
-
-
79957616672
-
Dynamic factor analysis models with time-varying parameters
-
Chow, S.-M., Zu, J., Shifren, K., & Zhang, G. (2011). Dynamic factor analysis models with time-varying parameters. Multivariate Behavioral Research, 46, 303-339.
-
(2011)
Multivariate Behavioral Research
, vol.46
, pp. 303-339
-
-
Chow, S.-M.1
Zu, J.2
Shifren, K.3
Zhang, G.4
-
4
-
-
79961225739
-
Statistical software for state space methods
-
Retrieved from
-
Commandeur, J. J. F., Koopman, S. J., & Ooms, M. (2011). Statistical software for state space methods. Journal of Statistical Software, 41, 1-18. Retrieved from www. jstatsoft. org/v41/i1/.
-
(2011)
Journal of Statistical Software
, vol.41
, pp. 1-18
-
-
Commandeur, J.J.F.1
Koopman, S.J.2
Ooms, M.3
-
5
-
-
0002629270
-
Maximum likelihood from incomplete data via the EM algorithm
-
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, XXX, 1-39.
-
(1977)
Journal of the Royal Statistical Society, Series B
, vol.XXX
, pp. 1-39
-
-
Dempster, A.P.1
Laird, N.M.2
Rubin, D.B.3
-
8
-
-
27844472136
-
Statistical modeling of the individual: Rational and application of multivariate stationary time series analysis
-
Hamaker, E. L., Dolan, C. V., & Molenaar, P. C. M. (2005). Statistical modeling of the individual: Rational and application of multivariate stationary time series analysis. Multivariate Behavioral Research, 40, 207-233.
-
(2005)
Multivariate Behavioral Research
, vol.40
, pp. 207-233
-
-
Hamaker, E.L.1
Dolan, C.V.2
Molenaar, P.C.M.3
-
11
-
-
33646131681
-
State-space approach to modeling dynamic processes: Applications in biological and social sciences
-
T. A. Walls and J. L. Schafer (Eds.), New York, NY: Oxford University Press
-
Ho, M.-H. R., Shumway, R., & Ombao, H. (2006). State-space approach to modeling dynamic processes: Applications in biological and social sciences. In T. A. Walls & J. L. Schafer (Eds.), Models for intensive longitudinal data (pp. 148-170). New York, NY: Oxford University Press.
-
(2006)
Models for Intensive Longitudinal Data
, pp. 148-170
-
-
Ho, M.-H.R.1
Shumway, R.2
Ombao, H.3
-
14
-
-
32544452484
-
A manifesto on psychology as idiographic science: Bring the person back into scientific psychology, this time forever
-
Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bring the person back into scientific psychology, this time forever. Measurement, 2, 201-218.
-
(2004)
Measurement
, vol.2
, pp. 201-218
-
-
Molenaar, P.C.M.1
-
16
-
-
84865493367
-
Advances in dynamic factor analysis of psychological processes
-
J. Valsiner, P. C. M. Molenaar, MCDPLyra, and N. Chaudhary (Eds.), Dordrecht, The Netherlands: Springer Science
-
Molenaar, P. C. M., & Ram, N. (2009). Advances in dynamic factor analysis of psychological processes. In J. Valsiner, P. C. M. Molenaar, M. C. D. P. Lyra, & N. Chaudhary (Eds.), Dynamic process methodology in the social and developmental sciences (pp. 255-268). Dordrecht, The Netherlands: Springer Science.
-
(2009)
Dynamic Process Methodology in the Social and Developmental Sciences
, pp. 255-268
-
-
Molenaar, P.C.M.1
Ram, N.2
-
17
-
-
84874380621
-
-
SAS Institute Inc, Cary, NC: SAS Institute Inc
-
SAS Institute Inc. (2010). SAS/IML®9. 22 user's guide. Cary, NC: SAS Institute Inc.
-
(2010)
SAS/IML®Spicsupspi 9.22 User's Guide
-
-
-
18
-
-
84939734910
-
Evaluation of likelihood functions for Gaussian signals
-
Schweppe, F. (1965). Evaluation of likelihood functions for Gaussian signals. IEEE Transactions on Information Theory, 11, 61-70.
-
(1965)
IEEE Transactions on Information Theory
, vol.11
, pp. 61-70
-
-
Schweppe, F.1
-
19
-
-
79961218198
-
State space modeling using SAS
-
Retrieved from
-
Selukar, R. (2011). State space modeling using SAS. Journal of Statistical Software, 41, 1-13. Retrieved from www. jstatsoft. org/v41/i12/.
-
(2011)
Journal of Statistical Software
, vol.41
, pp. 1-13
-
-
Selukar, R.1
-
20
-
-
84986753417
-
An approach to time series smoothing and forecasting using the EM algorithm
-
Shumway, R. H., & Stoffer, D. S. (1982). An approach to time series smoothing and forecasting using the EM algorithm. Journal of Time Series Analysis, 3, 253-264.
-
(1982)
Journal of Time Series Analysis
, vol.3
, pp. 253-264
-
-
Shumway, R.H.1
Stoffer, D.S.2
-
21
-
-
70449562577
-
State-space modeling of dynamic psychological processes via the Kalman smoother algorithm: Rationale, finite sample properties, and applications
-
Song, H., & Ferrer, E. (2009). State-space modeling of dynamic psychological processes via the Kalman smoother algorithm: Rationale, finite sample properties, and applications. Structural Equation Modeling, 16, 338-363.
-
(2009)
Structural Equation Modeling
, vol.16
, pp. 338-363
-
-
Song, H.1
Ferrer, E.2
-
22
-
-
0011520418
-
Bootstrapping state-space models: Gaussian maximum likelihood estimation and the Kalman filter
-
Stoffer, D. S., & Wall, K. D. (1991). Bootstrapping state-space models: Gaussian maximum likelihood estimation and the Kalman filter. Journal of the American Statistical Association, 86, 1024-1033.
-
(1991)
Journal of the American Statistical Association
, vol.86
, pp. 1024-1033
-
-
Stoffer, D.S.1
Wall, K.D.2
-
23
-
-
27644509950
-
Resampling in state spaace models
-
In A. Harvey, S. J. Koopman, & N. Shephar (Eds.), New York, NY: Cambridge University Press
-
Stoffer, D. S., & Wall, K. D. (2004). Resampling in state spaace models. In A. Harvey, S. J. Koopman, & N. Shephar (Eds.), State space and unobserved component models: Theory and applications. (pp. 171-202). New York, NY: Cambridge University Press.
-
(2004)
State space and unobserved component models: Theory and applications
, pp. 171-202
-
-
Stoffer, D.S.1
Wall, K.D.2
-
26
-
-
79952251387
-
Standard error estimation in stationary multivariate time series models using residual-based bootstrap procedures
-
P. C. M. Molenaar and K. M. Newell (Eds.), Washington, DC: American Psychological Association
-
Zhang, G., & Chow, S.-M. (2010). Standard error estimation in stationary multivariate time series models using residual-based bootstrap procedures. In P. C. M. Molenaar & K. M. Newell (Eds.), Individual pathway of change: Statistical models for analyzing learning and development (pp. 169-182). Washington, DC: American Psychological Association.
-
(2010)
Individual Pathway of Change: Statistical Models for Analyzing Learning and Development
, pp. 169-182
-
-
Zhang, G.1
Chow, S.-M.2
-
27
-
-
47949098250
-
Comparisons of four methods for estimating a dynamic factor model
-
Zhang, Z., Hamaker, E. L., & Nesselroade, J. R. (2008). Comparisons of four methods for estimating a dynamic factor model. Structural Equation Modeling, 15, 377-402.
-
(2008)
Structural Equation Modeling
, vol.15
, pp. 377-402
-
-
Zhang, Z.1
Hamaker, E.L.2
Nesselroade, J.R.3
|