The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Wang Shiqing - One of the best experts on this subject based on the ideXlab platform.
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the linear minimax estimator of regression coefficients in the variance component model under Quadratic Loss Function
Journal of Nanyang Normal University, 2008Co-Authors: Wang ShiqingAbstract:The variance component model is considered.For arbitrary estimable Function,the unique linear minimax estimator under a given matrix Loss Function is obtained in the class of homogeneous linear estimators.
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the minimax admissibility estimates of multivariate regression coefficient in the restricted growth curve model under Quadratic Loss Function
Journal of Nanyang Normal University, 2006Co-Authors: Wang ShiqingAbstract:In this paper,we consider the minimax admissibility estimates of the restricted multivariate regression coefficient under Quadratic Loss Function.The necessary and sufficient condition are given for a linear estimate MYN(MYN+C)to be minimax admissible in the class of some homogeneous(non-homogenous)linear estimates,and a minimax admissible estimate is given.
Argon Chen - One of the best experts on this subject based on the ideXlab platform.
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an economic design of xbar control chart using Quadratic Loss Function
International Journal of Production Research, 1994Co-Authors: Elsayed A Elsayed, Argon ChenAbstract:Abstract The Quadratic Loss Function is used in Taguchi's on-line cost model to estimate the quality cost. However, Taguchi's on-line quality control approach is different from the widely used statistical process control techniques where control charts are the primary tools for quality control. Duncan's economic design of x control chart is the first attempt to design the control charts in terms of process cost. In this paper, we present a new economic design based on the Loss Function approach as advocated by Taguchi. We also obtain the optimal parameters of the control chart that minimize the total quality cost.
Igor Nikiforov - One of the best experts on this subject based on the ideXlab platform.
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ALCOSP - Bayesian Test for Multiple Hypothesis Testing Problem with Quadratic Loss
IFAC Proceedings Volumes, 2020Co-Authors: Jian Zhang, Lionel Fillatre, Igor NikiforovAbstract:The Bayesian test with 0—1 Loss Function is a standard solution to solve a multiple hypothesis testing problem in the Bayesian framework. For a large number of applications (like the intrusion detection, the anomaly detection,…) the alternative hypotheses have quite different importance and 0—1 Loss Function does not reflect the reality. The Quadratic Loss Function can be more appropriate to distinguish the concurrent hypotheses. The main contribution of the paper is the design of the Bayesian test with a Quadratic Loss Function and its asymptotic study. When the signal-to-noise ratio tends to infinity, it is theoretically established that the error probabilities of the proposed test coincide with the error probabilities of the standard one associated to the 0—1 Loss Function. In the non-asymptotic case, the numerical experiments show that the proposed test outperforms the Bayesian test associated to the 0—1 Loss Function when compared by using the Quadratic Loss Function.
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Bayesian localization of anomaly in distributed networks with Quadratic criterion
Journal of Intelligent and Fuzzy Systems, 2017Co-Authors: Jian Zhang, Lionel Fillatre, Igor NikiforovAbstract:The anomaly localization in distributed networks can be treated as a multiple hypothesis testing (MHT) problem and the Bayesian test with 0-1 Loss Function is a standard solution to this problem. However, For the anomaly localization application, the cost of different false localization varies, which cannot be reflected by the 0 - 1 Loss Function while the Quadratic Loss Function is more appropriate. The main contribution of the paper is the design of a Bayesian test with a Quadratic Loss Function and its performance analysis. The non-asymptotic bounds of the misclassification probabilities of the proposed test and the standard one with 0-1 Loss Function are established and the relationship between their asymptotic equivalence with respect to signal-to-noise ratio and the geometry of the parameter space is analyzed. The effectiveness of the non-asymptotic bounds and the analysis on the asymptotic equivalence are verified by the simulation results.
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Bayesian Test with Quadratic Criterion for Multiple Hypothesis Testing Problem
2017 International Conference on Industrial Informatics - Computing Technology Intelligent Technology Industrial Information Integration (ICIICII), 2017Co-Authors: Jian Zhang, Lionel Fillatre, Igor NikiforovAbstract:A Bayesian test has been previously proposed for a multiple hypothesis testing (MHT) problem with a Quadratic Loss Function such that this problem can fit with some real applications where the concurrent hypotheses should be distinguished. However, this MHT problem as well as this Quadratic Loss Function are insufficient for some other applications such as the simultaneous intrusion detection and localization in a wireless sensor network (WSN). This kind of applications could be considered as a MHT problem with null hypothesis. Therefore, a Bayesian test with a modified Quadratic Loss Function is proposed to solve this MHT problem. The non-asymptotic bounds for analyzing the performance of the proposed test and the Bayesian test with the 0-1 Loss Function are obtained, from which the conditional asymptotic equivalence between these two tests is then theoretically established. The effectiveness of these bounds and the analysis on the conditional asymptotic equivalence are verified by the simulation results.
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Better Bounds for Bayesian Multiple Test with Quadratic Loss Function
2015 International Conference on Industrial Informatics - Computing Technology Intelligent Technology Industrial Information Integration, 2015Co-Authors: Jian Zhang, Lionel Fillatre, Igor NikiforovAbstract:A Bayesian test has been previously proposed for a multiple hypothesis testing problem given the 0-1 Loss Function. However, this Function is not suitable for many applications such as intrusion detection, anomaly detection where a Quadratic Loss Function can be more appropriate to distinguish the concurrent hypotheses. Although a Bayesian test with the Quadratic Loss Function has been constructed for this problem, its asymptotic performance has not yet been well studied due to its poor bounds. The main contribution of this paper is the construction of better bounds for this Bayesian test and the one associated with the 0-1 Loss Function. With these new bounds, it is theoretically established that the asymptotic equivalence between these two tests depends on the geometry of the parameter space associated with the hypotheses.
Elsayed A Elsayed - One of the best experts on this subject based on the ideXlab platform.
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an economic design of xbar control chart using Quadratic Loss Function
International Journal of Production Research, 1994Co-Authors: Elsayed A Elsayed, Argon ChenAbstract:Abstract The Quadratic Loss Function is used in Taguchi's on-line cost model to estimate the quality cost. However, Taguchi's on-line quality control approach is different from the widely used statistical process control techniques where control charts are the primary tools for quality control. Duncan's economic design of x control chart is the first attempt to design the control charts in terms of process cost. In this paper, we present a new economic design based on the Loss Function approach as advocated by Taguchi. We also obtain the optimal parameters of the control chart that minimize the total quality cost.
A. Almansa - One of the best experts on this subject based on the ideXlab platform.
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Adaptive control of manipulators with supervision of the sampling rate and free-parameters of the adaptation algorithm
Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251), 1999Co-Authors: A. AlmansaAbstract:An adaptive neural control scheme for mechanical manipulators is proposed. A supervisor is used to improve the system performances during the adaptation transients. The supervisor exerts two supervisory actions. The first one consists basically in updating the free-design adaptive controller, the Quadratic Loss Function is maintained sufficiently small. The second supervisory action consists basically of an online adjustment of the sampling period within an interval centered in a nominal value of the sampling period. The sampling period is selected so that the transient of the tracking error is improved.