Estimation of time-varying parameters in statistical models

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Estimation of time-varying parameters in statistical models: an optimization approach

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Annual Workshop on Computational Learning Theory archive
Proceedings of the tenth annual conference on Computational learning theory table of contents

Nashville, Tennessee, United States

Pages: 314 - 324

Year of Publication: 1997

ISBN:0-89791-891-6

Authors

Dimitris Bertsimas	Sloan School of Management and Operations Research Center, MIT Cambridge, MA
David Gamarnik	Operations Research Center, MIT Cambridge, MA
John N. Tsitsiklis	Laboratory for Information and Decision Sciences and Operations Research Center, MIT Cambridge, MA

Sponsors

AT&T Labs :
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Vanderbilt University : Vanderbilt University

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ACM New York, NY, USA

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Downloads (6 Weeks): 2, Downloads (12 Months): 11, Citation Count: 1

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Vladimir N. Vapnik, The nature of statistical learning theory, Springer-Verlag New York, Inc., New York, NY, 1995
	2	R. Eubank. Spline Smoothing and Nonparametric Regression. MDekker, New York, 1988.
	3	L. P. Devroye. The uniform convergence of nearest neighbor regression function estimators and their application in optimization. IEEE Trans.1nform.Theor-y 24,142-151,1978.
	4	M. Bazaara, H. Sherah, C. Shetti. Nonlinear Programming; Theory and Algorithms. Wiley, New York, I 993.
	5	Pollard. Convergence of Stochastic Processes. Springer-Verlag, 1984.
	6	David Haussler, DECISION THEORETIC GENERALIZATIONS OF THE PAC MODEL FORNEURAL NET AND OTHER LEARNING APPLICATIONS, University of California at Santa Cruz, Santa Cruz, CA, 1990