In this paper, we propose a relative recursive regression estimator for censored data defined by the stochastic approximation algorithm to deal with the presence of outliers or when the response is usually positive. W...
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In this paper, we propose a relative recursive regression estimator for censored data defined by the stochastic approximation algorithm to deal with the presence of outliers or when the response is usually positive. We give the central limit theorem and the strong pointwise convergence rate for our proposed nonparametric relative recursive estimators under some mild conditions. We finally developed a second generation plug-in bandwidth selection procedure.
In the present paper, we extend the work of Slaoui (Stat Sin 30:417-437, 2020) in the case of strong mixing data. Since, we are interested in nonparametric regression estimation, we focus on well adapted dependence st...
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In the present paper, we extend the work of Slaoui (Stat Sin 30:417-437, 2020) in the case of strong mixing data. Since, we are interested in nonparametric regression estimation, we focus on well adapted dependence structures based on mixing type conditions. We study the properties of these regression estimators and compare them with the nonparametric non-recursive regression estimator. The bias, variance and mean squared error are computed explicitly. We showed that using a selected wild bootstrap bandwidth procedure and a special stepsize, our proposed recursive regression estimators allowed us to obtain quite similar results compared to the non-recursive regression estimator under alpha-mixing condition in terms of estimation error and much better in terms of computational costs.
We propose and investigate a new kernel regression estimator based on the minimization of the mean squared relative error. We study the properties of the proposed recursive estimator and compare it with the recursive ...
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We propose and investigate a new kernel regression estimator based on the minimization of the mean squared relative error. We study the properties of the proposed recursive estimator and compare it with the recursive estimator based on the minimization of the mean squared error proposed by Slaoui (2018). It turns out that, with an adequate choice of the parameters, the proposed estimator performs better than the recursive estimator based on the minimization of the mean squared error. We illustrate these theoretical results through a real chemometric dataset. (C) 2019 Elsevier Inc. All rights reserved.
In this paper we propose an automatic selection of the bandwidth of the recursive kernel estimators of a probability density function defined by the stochastic approximation algorithm in the case of length-biased data...
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In this paper we propose an automatic selection of the bandwidth of the recursive kernel estimators of a probability density function defined by the stochastic approximation algorithm in the case of length-biased data. We compared our proposed plug-in method with the cross-validation method and the so-called smooth bootstrap bandwidth selector via simulations as well as a real data set. Results showed that, using the selected plug-in bandwidth and some special stepsizes, the proposed recursive estimators will be very competitive to the non-recursive one in terms of estimation error and much better in terms of computational costs.
Optimal control strategies are studied for DC microgrid management. The optimization strategies intend to achieve the desired tradeoff for fair load allocation, loss reduction, and quality enhancement of voltage. Cont...
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ISBN:
(纸本)9781728119380
Optimal control strategies are studied for DC microgrid management. The optimization strategies intend to achieve the desired tradeoff for fair load allocation, loss reduction, and quality enhancement of voltage. Continuing our previous work on distributed optimization algorithms for such problems, this paper considers DC microgrids with subsystem dynamics. Inclusion of subsystem dynamics accommodates many real systems such as converter dynamics, impacts performance significantly, and complicates system analysis. Expanded system dynamics are derived, stability and convergence analysis are carried out, and main properties are established. Case studies are performed to demonstrate our methods and their performance.
In this paper, we provide two new stable online algorithms for the problem of prediction in reinforcement learning, i.e., estimating the value function of a model-free Markov reward process using the linear function a...
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In this paper, we provide two new stable online algorithms for the problem of prediction in reinforcement learning, i.e., estimating the value function of a model-free Markov reward process using the linear function approximation architecture and with memory and computation costs scaling quadratically in the size of the feature set. The algorithms employ the multi-timescale stochasticapproximation variant of the very popular cross entropy optimization method which is a model based search method to find the global optimum of a real-valued function. A proof of convergence of the algorithms using the ODE method is provided. We supplement our theoretical results with experimental comparisons. The algorithms achieve good performance fairly consistently on many RL benchmark problems with regards to computational efficiency, accuracy and stability.
In this paper, we propose two kernel density estimators based on a bias reduction technique. We study the properties of these estimators and compare them with Parzen-Rosenblatt's density estimator and Mokkadem, A....
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In this paper, we propose two kernel density estimators based on a bias reduction technique. We study the properties of these estimators and compare them with Parzen-Rosenblatt's density estimator and Mokkadem, A., Pelletier, M., and Slaoui, Y. (2009, The stochasticapproximation method for the estimation of a multivariate probability density', J. Statist. Plann. Inference, 139, 2459-2478) is density estimators. It turns out that, with an adequate choice of the parameters of the two proposed estimators, the rate of convergence of two estimators will be faster than the two classical estimators and the asymptotic MISE (Mean Integrated Squared Error) will be smaller than the two classical estimators. We corroborate these theoretical results through simulations.
In the present paper we propose recursive general kernel-type estimators for spatial data defined by the stochastic approximation algorithm. We obtain the central limit theorem and strong pointwise convergence rate fo...
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In the present paper we propose recursive general kernel-type estimators for spatial data defined by the stochastic approximation algorithm. We obtain the central limit theorem and strong pointwise convergence rate for the nonparametric recursive general kernel type estimators under some mild conditions. Finally, we investigate the MISE of the proposed estimators and provide the optimal bandwidth. (C) 2018 Elsevier B.V. All rights reserved.
We propose a recursive distribution estimator using Robbins-Monro's algorithm and Bernstein polynomials. We study the properties of the recursive estimator, as a competitor of Vitale's distribution estimator. ...
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We propose a recursive distribution estimator using Robbins-Monro's algorithm and Bernstein polynomials. We study the properties of the recursive estimator, as a competitor of Vitale's distribution estimator. We show that, with optimal parameters, our proposal dominates Vitale's estimator in terms of the mean integrated squared error. Finally, we confirm theoretical result throught a simulation study.
For a kind of complex production processes, a new control method is proposed based on pattern recognition technology. It avoids structure design or parameter identification of a system model or controller. First of al...
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ISBN:
(纸本)9781728113128
For a kind of complex production processes, a new control method is proposed based on pattern recognition technology. It avoids structure design or parameter identification of a system model or controller. First of all, the moving trajectory is predicted through the classification of system pattern samples constructed by input-output classes. And then it is approximately described by a straight-line model. According to the model the system input of a class is obtained. At last, experimental simulations demonstrate the validity of the proposed control method and analysis of parameters affecting control performance is presented.
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