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1

United States. Congress. Senate. Committee on Banking, Housing, and Urban Affairs. Subcommittee on Securities and Investment. The implementation and future of decimalized markets: Hearing before the Subcommittee on Securities and Investment of the Committee on Banking, Housing, and Urban Affairs, United States Senate, One Hundred Seventh Congress, first session the current status of implementation of decimal pricing for securities, including an estimation of costs and benefits, May 24, 2001. Washington: U.S. G.P.O., 2002.

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2

Power system state estimation. Boston, Mass: Artech House, 2013.

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3

United States. National Aeronautics and Space Administration., ed. Parameter identification for nonlinear aerodynamic systems: First annual (second semiannual) technical report to the Aircraft Guidance and Controls Branch/489, Guidance and Control Division, National Aeronautics and Space Administration, Langley Research Center ... period covered, October 23, 1989 to October 22, 1990. [Washington, DC]: The Administration, 1990.

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4

Pillai, S. Unnikrishna, and Theodore I. Shim. Spectrum Estimation and System Identification. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4613-8318-5.

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5

Pillai, S. Unnikrishna. Spectrum Estimation and System Identification. New York, NY: Springer New York, 1993.

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6

J, Wales Terence, ed. Demand system specification and estimation. New York: Oxford University Press, 1992.

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7

Pieter, Eykhoff, Parks P. C, International Federation of Automatic Control., International Federation of Operational Research Societies., and IFAC/IFORS Symposium on Identifaction and System Parameter Estimation, (8th : 1988 : Beijing), eds. Identification and system parameter estimation. Oxford: Pergamon, 1990.

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8

I, Shim Theodore, ed. Spectrum estimation and system identification. New York: Springer-Verlag, 1993.

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9

Crassidis, John L. Optimal estimation of dynamic systems. Boca Raton: Chapman & Hall/CRC, 2004.

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10

Czerwin, Benjamin James. Modeling and Estimation for the Renal System. [New York, N.Y.?]: [publisher not identified], 2021.

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11

Gumerman, Robert C. Estimation of small system water treatment costs. Cincinnati, OH: U.S. Environmental Protection Agency, Water Engineering Research Laboratory, 1985.

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12

Abur, Ali. Power system state estimation: Theory and implementation. New York, NY: Marcel Dekker, 2004.

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13

C, Murphy Patrick. A methodology for airplane parameter estimation and confidence interval determination in nonlinear estimation problems. Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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14

Wyoming. State Highway Dept. Urban Transportation Planning Unit., Wyoming. State Highway Dept., and United States. Federal Highway Administration., eds. [Name of city] urban system study: Major street and highway system : report. [Cheyenne, Wyo.]: The Unit, 1988.

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15

Nash, John C. Nonlinear parameter estimation: An integrated system in BASIC. Ottawa, Ontario, Canada: Nash Information Services, 1995.

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16

Lake, R. W. AIRAIL: Preliminary system definition and cost estimation analysis. [Ottawa]: National Research Council Canada, Transportation Technology Program, 1990.

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17

Chambers, Marcus J. Estimation of a continuous time dynamic demand system. [Colchester]: University ofEssex, Dept. of Economics, 1989.

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18

Nash, John C. Nonlinear parameter estimation: An integrated system in BASIC. New York: M. Dekker, 1987.

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19

Gradient estimation via perturbation analysis. Boston: Kluwer Academic Publishers, 1991.

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20

Maine, Richard E. Identification of dynamic systems: Theory and formulation. Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1985.

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21

Recursive nonlinear estimation: A geometric approach. Berlin: Springer, 1996.

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22

Nicol, David. User's guide to the Reliabilty Estimation System Testbed (REST). [Washington, DC: National Aeronautics and Space Administration, 1992.

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23

Nicol, David M. User's guide to the Reliability Estimation System Testbed (REST). [Washington, DC: National Aeronautics and Space Administration, 1992.

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24

Kaplan, Jonathan D. Army Research Institute Validation-Estimation System (ARIVES) user's manual. Alexandria, Va: U.S. Army Research Institute for the Behavioral and Social Sciences, 1989.

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25

Tabrizi, M. H. N. Model estimation and prediction for a water management system. Sheffield: University of Sheffield, Dept. of Control Engineering, 1989.

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26

Guestrin, Elias Daniel. A novel head-free point-of-gaze estimation system. Ottawa: National Library of Canada, 2003.

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27

Zhang, Ke. Observer-Based Fault Estimation and Accomodation for Dynamic Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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28

Sunahara, Y. A method of boundary parameter estimation for a two-dimensional diffusion system under noisy observations. Hampton, Va: ICASE, 1987.

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29

M, Milanese, ed. Bounding approaches to system identification. New York: Plenum Press, 1996.

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30

Montana. Rail, Transit and Planning Division. Data and Statistics Bureau. Planning Systems Section. Rural congestion management system (CoMS): 2005 report. [Helena?, Mont.]: Montana Dept. of Transportation, 2005.

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31

Ot͡s︡enivanie fazovogo sostoi͡a︡nii͡a︡ dinamicheskikh sistem: Metod ėllipsoidov. Moskva: "Nauka," Glav. red. fiziko-matematicheskoĭ lit-ry, 1988.

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32

Iliff, Kenneth W. Extraction of aerodynamic parameters for aircraft at extreme flight conditions. Edwards, Calif: National Aeronautics and Space Administration, Ames Research Center, Dryden Flight Research Facility, 1985.

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33

Walter, E. Identification of parametric models from experimental data. Berlin: Springer, 1997.

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34

Iliff, Kenneth W. Extraction of aerodynamic parameters for aircraft at extreme flight conditions. Edwards, Calif: National Aeronautics and Space Administration, Ames Research Center, Dryden Flight Research Facility, 1985.

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35

Facility, Dryden Flight Research, ed. Extraction of aerodynamic parameters for aircraft at extreme flight conditions. Edwards, Calif: National Aeronautics and Space Administration, Ames Research Center, Dryden Flight Research Facility, 1985.

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36

Schuermans, Stefan, and Rainer Leupers. Power Estimation on Electronic System Level using Linear Power Models. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-01875-7.

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37

IFAC/IFORS Symposium on Identification and System Parameter Estimation (7th 1985 York). Identification and system parameter estimation 1985: 7th IFAC/IFORS Symposium. Oxford: Published for the International Federation of Automatic Control by Pergamon, 1985.

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38

1952-, Kunisch K., ed. Estimation techniques for distributed parameter systems. Boston: Birkhäuser, 1989.

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39

Science Applications International Corporation. Space Sciences Dept. Advanced Planning and Analysis Division. and Lyndon B. Johnson Space Center. Engineering Directorate., eds. Satellite servicing price estimation instruction booklet. Schaumburg, IL: Advanced Planning and Analysis Division, Space Sciences Dept., Science Applications International Corporation, 1987.

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40

L, Junkins John, ed. Optimal estimation of dynamic systems. 2nd ed. Boca Raton, FL: Chapman and Hall/CRC, 2011.

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41

Kumar, R. A novel multireceiver communications system configuration based on optimal estimation theory. Pasadena, Calif: Jet Propulsion Laboratory, 1990.

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42

Lui, Brian J. M. A point-of-gaze estimation system for studies of visual attention. Ottawa: National Library of Canada, 2003.

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43

Verhagen, Sandra. The GNSS integer ambiguities: estimation and validation. Delft: NCG, Nederlandse Commissie voor Geodesie Netherlands Geodetic Commission, 2005.

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44

Subhendu, Das, Palumbo Daniel L, and Institute for Computer Applications in Science and Engineering., eds. Parallelized reliability estimation of reconfigurable computer networks. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1990.

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45

Walter, E. Identifiability of parametric models. Oxford [Oxfordshire]: Pergamon Press, 1987.

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46

IFAC/IFORS Symposium on Identification and System Parameter Estimation. (7th 1985 York, North Yorkshire). Identification and system parameter estimation, 1985: Proceedings of the seventh IFAC/IFORS Symposium. Oxford [Oxfordshire]: Published for the International Federation of Automatic Control by Pergamon Press, 1985.

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47

Lin, Lao-Sheng. Real-time estimation of ionospheric delay using GPS measurements. Sydney, NSW: School of Geomatic Engineering, University of New South Wales, 1998.

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48

Fu, Michael. Conditional Monte Carlo: Gradient estimation and optimization applications. Boston: Kluwer Academic Publishers, 1997.

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49

Nagy, Tom. Building your first expert system. Culver City, CA: Ashton-Tate, 1985.

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50

Beenakker, Carlo W. J. Extreme eigenvalues of Wishart matrices: application to entangled bipartite system. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.37.

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Abstract:
This article describes the application of random matrix theory (RMT) to the estimation of the bipartite entanglement of a quantum system, with particular emphasis on the extreme eigenvalues of Wishart matrices. It first provides an overview of some spectral properties of unconstrained Wishart matrices before introducing the problem of the random pure state of an entangled quantum bipartite system consisting of two subsystems whose Hilbert spaces have dimensions M and N respectively with N ≤ M. The focus is on the smallest eigenvalue which serves as an important measure of entanglement between the two subsystems. The minimum eigenvalue distribution for quadratic matrices is also considered. The article shows that the N eigenvalues of the reduced density matrix of the smaller subsystem are distributed exactly as the eigenvalues of a Wishart matrix, except that the eigenvalues satisfy a global constraint: the trace is fixed to be unity.
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