Abstract
The stochastic realization problem asks for the existence and the classification of all stochastic systems for which the output process equals a given process in distribution or almost surely. This is a fundamental problem of system and control theory. The stochastic realization problem is of importance to modelling by stochastic systems in engineering, biology, economics etc. Several stochastic systems are mentioned for which the solution of the stochastic realization problem may be useful. As an example recent research on the stochastic realization problem for the Gaussian factor model and a Gaussian factor system is discussed.
Preview
Unable to display preview. Download preview PDF.
References
N.L. Aggarwal (1974). Sur l'information de Fisher. Théories de l'Information, Lecture Notes in Mathematics 398, Springer-Verlag, Berlin, 111–117.
D.J. Aigner and M. Deistler (1989). Latent variables models — Editor's introduction. J. Econometrics 41, 1–3.
H. Akaike (1976). Canonical correlation analysis of time series and the use of an information criterion. R. K. Mehra, D. G. Lainiotis (eds.), System identification — Advances and case studies, Academic Press, New York, 27–96.
B. D. O. Anderson (1985). Identification of scalar errors-in-variables models with dynamics. Automatica J.-IFAC 21, 709–716.
B. D. O. Anderson and M. Deistler (1984). Identifiability in dynamic errors-in-variables models. J. Time Series Anal. 5, 1–13.
B. D. O. Anderson and M. Deistler (1987). Dynamic errors-in-variables systems with three variables. Automatica J.-IFAC 23, 611–616.
T. W. Anderson and H. Rubin (1956). Statistical inference in factor analysis. J. Neyman (ed.). Proc. Third Berkeley Symposium on Mathematical Statistics and Probability, Volume V, University of California Press, Berkeley, 111–150.
M. Aoki (1988). Nonstationarity, cointegration, and error correction in economic modeling. J. Econ. Dyn. Control 12, 199–201.
M. Aoki (1988). On alternative state space representations of time series models. J. Econ. Dyn. Control 12, 595–607.
V. I. Arkin and I. V. Evstigneev (1987). Stochastic models of control and economic dynamics, Academic Press, New York.
K. S. Arun, D. V. Bhaskar Rao, and S. Y. Kung (1983). A new predictive efficiency criterion for approximate stochastic realization. 1983 Conference on Decision and Control, IEEE, 1353–1355.
K. S. Arun and S. Y. Kung (1984). State space modeling and approximate realization methods for ARMA spectral estimation. IEEE International Conference on Systems, Man and Cybernetics, IEEE.
P. A. Bekker and J. De Leeuw (1987). The rank of reduced dispersion matrices. Psychometrika 52, 125–135.
A. Bensoussan and J. H. Van Schuppen (1985). Optimal control of partially observable stochastic systems with an exponential-of-integral performance index. SIAM J. Control Optim. 23, 599–613.
D. Blackwell and L. Koopmans (1957). On the identificability problem for functions of finite Markov chains. Ann. Math. Statist. 28, 1011–1015.
M. Deistler (1986). Lincar errors-in-variables models. S. Bittanti (ed.). Time series and linear systems, Lecture Notes in Control and Information Sciences, Springer-Verlag, Berlin, 37–67.
M. Deistler and B.D.O. Anderson (1988). Linear dynamic errors in variables models: Some structure theory. A. Bensoussan, J.L. Lions (eds.). Analysis and optimization of systems, Lecture Notes in Control and Information Sciences III, Springer-Verlag, Berlin, 873–883.
M. Deistler and B.D.O. Anderson (1989). Linear dynamic errors-in-variables models — Some structure theory. J. Econometrics 41, 39–63.
B. De Moor (1988). Mathematical concepts and techniques for modelling of static and dynamic systems, Thesis, Katholicke Universitcit Leuven, Leuven.
J. P. Dufour (1983). Resultats generiques en analyse factorielle, Department de Mathématiques, U.S.T.L., Montpellier.
R. Engle and M. Watson (1981). A one-factor multivariate time series model of metropolitan wage rates. J. Amer. Statist. Assoc. 76, 774–781.
R. F. Engle and C. W. J. Granger (1987). Co-integration and error correction: Representation estimation, and testing. Econometrica 55, 251–276.
P. Faurre (1976). Stochastic realization algorithms. R. K. Mehra, D. G. Lainiotis (eds.). System Identification — Advances and Case Studies, Academic Press, New York, 1–25.
P. Faurre, M. Clerget, and F. Germain (1979). Opérateurs rationnels positifs, Dunod, Paris.
R. Frisch (1934). Statistical confluence analysis by means of complete regression systems, Publ. no. 5, University of Oslo Economic Institute, Oslo.
J.F. Geweke and K.J. Singleton (1981). Maximum likelihood ‘confirmatory’ factor analysis of econometric time series. Int. Economic Rev. 22, 37–54.
R. D. Gittens (1985). Canonical analysis-A review with applications in ecology, Springer-Verlag, Berlin.
K. Glover and E. Jonckheere (1986). A comparison of two Hankel-norm methods for approximating spectra. C.I. Byrnes, A. Lindquist (eds.). Modelling, Identification and Robust Control, Elsevier Science Publishers B.V. (North-Holland), Amsterdam, 297–306.
A. Gombani and M. Pavon (1985). On the Hankel-norm approximation of linear stochastic systems. Systems & Control Lett. 5, 283–288.
A. Gombani and M. Pavon (1986). On approximate recursive prediction of stationary stochastic processes. Stochastics 17, 125–143.
A. Gombani, M. Pavon, and B. Coppo (1986). On Hankel-norm approximation of stationary increment processes. C.I. Byrnes, A. Lindquist (eds.). Modelling, Identification and Robust Control, Elsevier Science Publishers B.V., Amsterdam, 307–323.
C. W. J. Granger (1981). Some properties of time series data and their use in econometric model specification. J. Econometrics, 121–130.
M. Green and B. D. O. Anderson (1986). Identification of multivariable errors in variable models with dynamics. IEEE Trans. Automatic Control 31, 467–471.
E. J. Hannan and M. Deistler (1988). The statistical theory of linear systems, John Wilcy & Sons, New York.
C. Heij (1988). Deterministic identification of dynamical systems, Thesis, University of Groningen, Groningen.
N. P. Jewell and P. Bloomfield (1983). Canonical correlations of past and future for time series: Definitions and theory. Ann. Statist. 11, 837–847.
N. P. Jewell, P. Bloomfield, and F. C. Bartmann (1983). Canonical correlations of past and future for time series: Bounds and computation. Ann. Statist. 11, 848–855.
E. A. Jonckheere and J. W. Helton (1985). Power spectrum reduction by optimal Hankel norm approximation of the phase of the outer spectral factor. IEEE Trans. Automatic Control 30, 1192–1201.
R. E. Kalman (1982). System identification from noisy data. A.R. Bednarek, L. Cesari (eds.). Dynamical Systems II, Academic Press, New York, 135–164.
R. E. Kalman (1982). Identification from real data. M. Hazewinkel, A. H. G. Rinnooy Kan (eds.). Current developments in the interface: Economics, Econometrics, Mathematics, D. Reidel Publishing Company, Dordrecht, 161–196.
R. E. Kalman (1983). Identifiability and modeling in econometrics. P.R. Krishnaiah (ed.). Developments in Statistics 4, Academic Press, New York, 97–136.
R. E. Kalman, P.L. Falb, and M.A. Arbib (1969). Topics in mathematical system theory, McGraw-Hill Book Co., New York.
D. Kazakos and P. Papantoni-Kazakos (1980). Spectral distance measures between Gaussian processes. IEEE Trans. Automatic Control 25, 950–959.
S. Y. Kung and K. S. Arun (1983). Approximate realization methods for ARMA spectral estimation. 1983 IEEE International Symposium on Circuits and Systems, IEEE, 105–109.
W. E. Larimore, S. Mahmoud, and R. K. Mehra (1984). Multivariate adaptive model algorithmic control. Proceedings 23rd Conference on Decision and Control, IEEE, 675–680.
W. E. Larrimore (1983). System identification, reduced-order filtering and modeling via canonical variate analysis. Proceedings 1983 American Control Conference, 445–451.
A. Lindquist and G. Picci (1985). Realization theory for multivariate stationary Gaussian processes. SIAM J. Control Optim. 23, 809–857.
A. Lindquist, G. Picci, and G. Ruckebusch (1979). On minimal splitting subspaces and Markovian representations. Math. Systems Th. 12, 271–279.
M. Pavon (1984). Canonical correlations of past inputs and future outputs for linear stochastic systems. Syst. Control Lett. 4, 209–215.
A. Paz (1971). Introduction to probabilistic automata, Academic Press, New York.
V. V. Pellar and S. V. Khrushchev (1982). Hankel operators, best approximations, and stationary Gaussian processes. Russian Math. Surveys 37, 61–144.
G. Picci (1978). On the internal structure of finite-state stochastic processes. Proc. of a U.S.-Italy Seminar, Lecture Notes in Economics and Mathematical Systems 162, Springer-Verlag, Berlin, 288–304.
G. Picci (1989). Parametrization of factor analysis models. J. Econometrics 41, 17–38.
G. Picci and S. Pinzoni (1986). Factor analysis models for stationary stochastic processes. A. Bensoussan, J. L. Lions (eds.). Analysis and Optimization of Systems, Lecture Notes in Control and Information Sciences 83, Springer-Verlag, Berlin, 412–424.
G. Picci and S. Pinzoni (1986). Dynamic factor-analysis models for stationary processes. IMA J. Math. Control and Info. 3, 185–210.
P. Purdue (1979). Stochastic compartmental models: A review of the mathematical theory with ecological applications. J.H. Matis, B.C. Patten, G.C. White (eds.). Compartmental analysis of ecosystem models, International Co-operative Publishing House, Fairland, MD, 223–260.
C. R. Rao (1964). The use and interpretation of principal component analysis in applied research. Sankhya, Series A 26, 329–358.
O. Reiersøl (1941). Confluence analysis by means of lag moments and other methods of confluence analysis. Econometrica 9, 1–24.
O. Reiersøl (1945). Confluence analysis by means of instrumental sets of variables. Arkiv für Mathematik, Astronomi och Fysik 32A.
J. Rissanen (1978). Modeling by shortest data description. Automatica J. IFAC 14, 465–471.
J. Rissanen (1983). A universal prior for integers and estimation by minimum description length. Ann. Statist. 11, 416–431.
J. Rissanen (1985). Modeling by the minimum description lenghth principle-A survey, Preprint.
J. Rissanen (1986). Stochastic complexity and modeling. Ann. Statist. 14, 1080–1100.
J. Rissanen (1986). Predictive and nonpredictive minimum description length principles. S. Bittanti (ed.). Time series and linear systems, Lecture Notes in Control and Information Sciences 86, Springer-Verlag, Berlin, 115–140.
J. Ruckebusch (1978). Sur l'approximation rationnelle des filtres, Centre de Mathématiques Appliquees, Ecole Polytechnique, Palaiseau.
P.A. Samuelson (1979). A note on alternative regressions. J.E. Stiglitz (ed.). The collected scientific papers of Paul A. Samuelson, Fifth Printing, M.I.T. Press, Cambridge, 694–697.
C.A. Spearman (1904). General intelligence, objectively determined and measured. Amer. J. Psych. 15, 201–293.
P.J.C. Spreij (1986). Selfexciting counting process systems with finite state space, Report OS-R8613, Centre for Mathematics and Computer Science, Amsterdam.
P.J.C. Spreij (1987). Counting process systems-Identification and stochastic realization, Doctoral Thesis, University of Twente, Enschede.
J. H. Steiger (1979). Factor indeterminacy in the 1930's and the 1970's some interesting parallels. Psychometrika 44, 157–167.
C. Van Putten and J.H. Van Schuppen (1983). The weak and strong Gaussian probabilistic realization problem. J. Multivariate Anal. 13, 118–137.
J.H. Van Schuppen (1979). Stochastic filtering theory: A discussion of concepts, methods and results. M. Kohlmann, W. Vogel (eds.). Stochastic control theory and stochastic differential systems, Lecture Notes in Control and Information Sciences 16, Springer-Verlag, Berlin, 209–226.
J. H. Van Schuppen (1982). The strong finite stochastic realization problem-Preliminary results. A. Bensoussan, J.L. Lions (eds.). Analysis and optimization of systems, Lecture Notes in Control and Information Sciences 44, Springer-Verlag, Berlin, 179–190.
J.H. Van Schuppen (1986). Stochastic realization problems motivated by econometric modeling. C.I. Byrnes, A. Lindquist (eds.). Modelling, Identification and Robust Control, Elsevier Science Publishers B. V. (North-Holland), Amsterdam, 259–275.
E. Verriest (1986). Projection Techniques for model reduction. C.I. Byrnes, A. Lindquist (eds.). Modelling, Identification and Robust Control, Elsevier Science Publishers B.V. (North-Holland), Amsterdam, 381–396.
J.V. White (1983). Stochastic state-space models from empirical data. Proceedings 1983 Conference on Acoustics, Speechs and Signal Processing, IEEE, 243–246.
P. Whittle (1981). Risk-sensitive linear/quadratic/Gaussian control. Adv. Appl. Prob. 13, 764–777.
J.C. Willems (1986). From time series to linear systems-Part I. Finite dimensional linear time invariant systems. Automatica J. IFAC 22, 561–580.
J.C. Willems (1987). From time series to linear systems-Part III. Approximate modelling. Automatica J. IFAC 23, 87–115.
E. B. Wilson (1929). Review of ‘Crossroads in the mind of man: A study of differentiable mental abilities’ by T.L. Kelley. J. Gen. Psychology 2, 153–169.
Author information
Authors and Affiliations
Editor information
Additional information
This paper is dedicated to J. C. Willems on the occasion of his fiftieth birthday.
Rights and permissions
Copyright information
© 1989 Springer-Verlag
About this chapter
Cite this chapter
van Schuppen, J.H. (1989). Stochastic realization problems. In: Nijmeijer, H., Schumacher, J.M. (eds) Three Decades of Mathematical System Theory. Lecture Notes in Control and Information Sciences, vol 135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0008474
Download citation
DOI: https://doi.org/10.1007/BFb0008474
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-51605-7
Online ISBN: 978-3-540-46709-0
eBook Packages: Springer Book Archive