L.H. Lee and K. Poolla. Statistical validation for uncertainty models. In B. Francis and A.R. Tannenbaum, editors, Feedback Control, Nonlinear Systems, and Complexity, pages 131-149, Springer Verlag, London, 1995. Home/Search Document Details and Download
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Statistical Learning Control of Uncertain Systems: It.. - Koltchinskii.. (1999) (1 citation) (Correct)
....Hoe ding Inequality, and other elementary probabilistic tools [21] 34] 66] with ideas advanced during the 1960s and 1970s [63] on the theory of empirical processes and statistical learning. In control theory, some of the original (Monte Carlo) ideas have already been used by Lee and Poolla [45], Ray and Stengel [52] Tempo et al. 6] 59] 60] Barmish et al. 7] 8] 9] 10] Chen and Zhou [18] 19] 20] and by Khargonakar and Tikku [40] to solve robust analysis problems while Vidyasagar used learning theory to solve robust design problems [66] 68] Unfortunately, and as ....
L.H. Lee and K. Poolla. Statistical validation for uncertainty models. In B. Francis and A.R. Tannenbaum, editors, Feedback Control, Nonlinear Systems, and Complexity, pages 131-149, Springer Verlag, London, 1995.
On Statistical Model Validation - Lee, Poolla (1994) (1 citation) Self-citation (Poolla) (Correct)
....model validation problem for parametric uncertainty models. For the general case of nonparametric models, the situation is significantly more complicated; there arises the question of whether or not we should even attempt to statistically validate such models. These and other issues are treated in [13] and [14] In this paper, we show (using a Bayesian approach) that in many cases of interest the statistical model validation problem reduces to computation of relative volumes of convex sets in R N (N being the number of uncertain parameters) weighted by an appropriately conditioned noise ....
....existing Hit and Run algorithm. By applying ideas from hypothesis testing, the statistical model validation problem can be phrased as a statistical decision problem that reduces to likelihood ratio testing. We will not present the specifics of these results here; the interested reader may consult [13] and [14] for more detailed discussions of the connection between statistical model validation and hypothesis testing. The remainder of this paper is organized as follows. In Section 2 we establish notation. Following this, in Section 3 we formulate the statistical model validation problem and ....
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L. Lee and K. Poolla, "Statistical Validation for Uncertainty Models," Control, Complexity, and Identification: A festschrift for Professor George Zames, Springer-Verlag, 1994.
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