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85 results on '"Verriest, E."'

1. Time Variant Balancing and Nonlinear Balanced Realizations

Author

Verriest, E. I., Bock, Hans-Georg, editor, de Hoog, Frank, editor, Friedman, Avner, editor, Gupta, Arvind, editor, Neunzert, Helmut, editor, Pulleyblank, William R., editor, Rusten, Torgeir, editor, Santosa, Fadil, editor, Tornberg, Anna-Karin, editor, Bonilla, Luis L., editor, Mattheij, Robert, editor, Scherzer, Otmar, editor, Schilders, Wilhelmus H. A., editor, van der Vorst, Henk A., editor, and Rommes, Joost, editor

Published
2008
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2. Canonical Variate Modeling and Approximation of Linear Systems

Author

Ramos, J. A., Verriest, E. I., Byrnes, Christopher I., editor, Amari, S.-I, editor, Anderson, B. D. O., editor, Äström, Karl J., editor, Aubin, Jean-Pierre, editor, Banks, H. T., editor, Baras, John S., editor, Bensoussan, A., editor, Burns, John, editor, Chen, Han-Fu, editor, Davis, M. H. A., editor, Fleming, Wendell, editor, Fliess, Michel, editor, Glover, Keith, editor, Hinrichsen, D., editor, Isidori, Alberto, editor, Jakubczyk, B., editor, Kimura, Hidenori, editor, Krener, Arthur, editor, Kunita, H., editor, Kurzhansky, Alexandre, editor, Kushner, Harold M., editor, Lindquist, Anders, editor, Manitius, Andrzej, editor, Martin, Clyde F., editor, Mitter, Sanjoy, editor, Picci, Giorgio, editor, Pshenichnyj, Boris, editor, Sussmann, H. J., editor, Tarn, T. J., editor, Tikhomirov, V. M., editor, Varaiya, Pravin P., editor, Willems, Jan C., editor, Wonham, W. M., editor, Kaashoek, M. A., editor, van Schuppen, J. H., editor, and Ran, A. C. M., editor

Published
1990
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6. The Variance of Logarithms: 10690

Author

Helmbold, Robert, Kim, Wook, Agnew, R. A., Ahmed, Z., Amghibech, S., Andersen, K. F., Anderson, P. J., Bower, G., Burdick, B. S., Chapman, R. J., Donini, D., Fitzsimmons, P. J., Groeneveld, R. A., Hernandez, V., Martin, J., Hertz, E., Huff, B. W., Keselman, G., Kim, S. S., Leslie, R. A., Lindsey, J. H., Lossers, O. P., Martin, R., Nester, D. K., Peng, G., Schindler, V., Scribani, L., Simeonov, P., Stein, A. H., Tyler, D. B., and Verriest, E. I.

Published
2000
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8. Revealed preference tests for collective household behavior

Author

Cherchye, L.J.H., de Rock, B., Vermeulen, F.M.P., Verriest, E., Molina, J.A., Research Group: Econometrics, Research Group: Operations Research, and Econometrics and Operations Research

Abstract

This chapter contains a state of the art of revealed preference tests for consistency of observed household behavior with Pareto efficiency. These tests are entirely nonparametric, since they do not require any assumptions regarding the parametric form of individual preferences or the intrahousehold bargaining process. We start with a discussion of some tests that are based on Chiappori’s (Econometrica, 56, 63–89, 1988) seminal labor supply model with egoistic preferences and observed individual leisure. We then present revealed preference conditions for Browning and Chiappori’s (Econometrica, 66, 1241–1278, 1998) collective consumption model with general individual preferences (including public goods and externalities) and only aggregate household consumption observed. Finally, we deal with a test for special cases of the general model, one that is based on integer programming.

Published
2011

25. Linear Markov approximations of piecewise linear stochastic systems

Author

Verriest, E. I. and Haddad, A. H.

Abstract

This paper is concerned with the properties of piecewise linear discrete-time dynamic systems driven by white Gaussian noise. The properties of the deterministic system are explored, and condition for the existence of invariant distributions are derived. The existence of an invariant distribution was then used to justify the approximation of the stochastic system by a switched Markov linear model if the piecewise linear regions are large "contracting" ones or small "expanding" ones relative to the input noise variance. The approach is expected to be useful for constructing approximate nonlinear filtering schemes for such systems

Published
1987
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30. Square-Root Algorithms for Model Sensitivity Analysis.

Author

STANFORD UNIV CALIF DEPT OF ELECTRICAL ENGINEERING, Morf,M, Verriest,E, Dobbins,J, Kailath,T, STANFORD UNIV CALIF DEPT OF ELECTRICAL ENGINEERING, Morf,M, Verriest,E, Dobbins,J, and Kailath,T

Abstract

The Morf-Kailath discrete square-root filtering algorithms are extended to incorporate sensitivity information with respect to the assumed model and statistics is available, considered to be the actual model. The sensitivity analysis is carried out by computing the so-called variance ratios that can be obtained by a second set of orthogonal transforms. These operations can be performed either in covariance or information filter form, as the actual filters. This leads to four basic variants for the propagation of the variance ratios. These ideas can also be applied to other related areas such as smoothing and the dual control problems, where it is of interest to study the sensitivity of the total cost with respect to different models and weights of the cost., Proceedings of the 1977 Conference on Information Sciences and Systems, Held at Johns Hopkins Univ., MD, 30 Mar-1 Apr 77, p64-69.

Published
1977

31. Approximations and Implementations of Nonlinear Filtering Schemes.

Author

GEORGIA INST OF TECH ATLANTA SCHOOL OF ELECTRICAL ENGINEERING, Haddad, A H, Verriest, E I, GEORGIA INST OF TECH ATLANTA SCHOOL OF ELECTRICAL ENGINEERING, Haddad, A H, and Verriest, E I

Abstract

The objective of this program is to develop approximation models that may be used for the implementation of nonlinear filtering schemes. The approach is based on two major concepts. The first is concerned with the approximation of nonlinear models for air-to-air missile scenarios by a Markov linear approximate model. The second concept is concerned with models for the errors due to the finite word-length implementation of the filters. In addition, the project is involved with the study of hybird system models that result when nonlinear or linear models such as these are quantized and are subject to switches among a set of linear models. The switching can either be deterministic or stochastic and may involve fast and slow dynamics., Errata sheet inserted.

Published
1988

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