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Comparison of Some of Estimation methods of Stress-Strength Model: R = P(Y < X < Z)
مجلة بغداد للعلوم, 2021 In this study, the stress-strength model R = P(Y < X < Z) is discussed as an important parts of reliability system by assuming that the random variables follow Invers Rayleigh Distribution. Some traditional estimation methods are used to estimate the
Sairan Hamza Raheem +2 more
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Generalized robust shrinkage estimator and its application to STAP detection problem [PDF]
, 2014 Recently, in the context of covariance matrix estimation, in order to improve as well as to regularize the performance of the Tyler's estimator [1] also called the Fixed-Point Estimator (FPE) [2], a "shrinkage" fixed-point estimator has been introduced ...
Chitour, Yacine +2 more
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Shrinkage Estimation of the Power Spectrum Covariance Matrix [PDF]
, 2008 We seek to improve estimates of the power spectrum covariance matrix from a limited number of simulations by employing a novel statistical technique known as shrinkage estimation.
Adrian C. Pope +14 more
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Bayesian Fusion Estimation via t Shrinkage [PDF]
Sankhya A, 2019 Shrinkage prior has gained great successes in many data analysis, however, its applications mostly focus on the Bayesian modeling of sparse parameters. In this work, we will apply Bayesian shrinkage to model high dimensional parameter that possesses an unknown blocking structure.
Qifan Song, Guang Cheng
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Applying Shrinkage Estimation Technique of P(Y
Ibn Al-Haitham Journal for Pure and Applied Sciences, 2020 This paper concerned with estimation reliability (Â for K components parallel system of the stress-strength model with non-identical components which is subjected to a common stress, when the stress and strength follow the Generalized Exponential ...
Adel Abdulkadhim Hussein +2 more
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Conditions for Posterior Contraction in the Sparse Normal Means Problem [PDF]
, 2015 The first Bayesian results for the sparse normal means problem were proven for spike-and-slab priors. However, these priors are less convenient from a computational point of view.
Salomond, Jean-Bernard +2 more
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IEEE Access, 2018 This paper addresses the estimation of large-dimensional covariance matrices under both normal and nonnormal distributions. The shrinkage estimators are constructed by convexly combining the sample covariance matrix and a structured target matrix.
Jianbo Li, Jie Zhou, Bin Zhang
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Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator [PDF]
Operations Research, 2022 Note. The best result in each experiment is highlighted in bold.The optimal solutions of many decision problems such as the Markowitz portfolio allocation and the linear discriminant analysis depend on the inverse covariance matrix of a Gaussian random vector.
Nguyen, Viet Anh +2 more
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On Reliability Estimation for the Exponential Distribution Based on Monte Carlo Simulation
Ibn Al-Haitham Journal for Pure and Applied Sciences, 2018 This Research deals with estimation the reliability function for two-parameters Exponential distribution, using different estimation methods ; Maximum likelihood, Median-First Order Statistics, Ridge Regression, Modified Thompson-Type Shrinkage ...
Abbas Najim Salman, Taha Anwar Taha
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Nonlinear shrinkage estimation of large-dimensional covariance matrices [PDF]
, 2011 Many statistical applications require an estimate of a covariance matrix and/or its inverse. When the matrix dimension is large compared to the sample size, which happens frequently, the sample covariance matrix is known to perform poorly and may suffer ...
Ledoit, Olivier, Wolf, Michael
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