Results 21 to 30 of about 34,245 (290)
Sequential Shrinkage Estimation
The Annals of Statistics, 1987 Let \(X_ 1,X_ 2,\ldots\) \((p\times 1)\) be i.i.d. \(N(\theta,\sigma^2V)\), with \(\theta\), \(\sigma\) unknown and \(V\) a known \(p\times p\) positive definite matrix. If it is decided to stop at stage \(n\) and \(\theta\) is estimated by \(\delta_ n=\delta_ n(X_ 1,\ldots,X_ n)\), then the loss will be \(L(\theta,\delta_ n)'Q(\delta_ n-\theta)+cn ...
Ghosh, Malay +2 more
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A Robust Statistics Approach to Minimum Variance Portfolio Optimization [PDF]
, 2015 We study the design of portfolios under a minimum risk criterion. The performance of the optimized portfolio relies on the accuracy of the estimated covariance matrix of the portfolio asset returns.
Couillet, Romain +2 more
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Comparison of Risk Ratios of Shrinkage Estimators in High Dimensions
Mathematics, 2021 In this paper, we analyze the risk ratios of several shrinkage estimators using a balanced loss function. The James–Stein estimator is one of a group of shrinkage estimators that has been proposed in the existing literature.
Abdenour Hamdaoui +3 more
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Open Mathematics, 2022 One of the most common challenges in multivariate statistical analysis is estimating the mean parameters. A well-known approach of estimating the mean parameters is the maximum likelihood estimator (MLE).
Benkhaled Abdelkader +4 more
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Small Area Shrinkage Estimation
Statistical Science, 2012 The need for small area estimates is increasingly felt in both the public and private sectors in order to formulate their strategic plans. It is now widely recognized that direct small area survey estimates are highly unreliable owing to large standard errors and coefficients of variation.
Datta, G., Ghosh, M.
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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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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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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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The Bootstrap Method for the Selection of a Shrinkage Factor in Two-stage Estimation of the Reliability Function of an Exponential Distribution [PDF]
, 2009 An application of a bootstrap method for selecting a suitable shrinkage factor for the two-stage shrinkage estimator of a reliability function for the exponential distribution is discussed. The estimator obtained here has higher efficiency as compared to
Ratnaparkhi, Makarand V. +2 more
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Improving generalized ridge estimator for the gamma regression model. [PDF]
المجلة العراقية للعلوم الاحصائيةIt has been consistently proven that the ridge estimator is an effective shrinking strategy for reducing the effects of multicollinearity. An effective model to use when the response variable is positively skewed is the Gamma Regression Model (GRM ...
AVAN Al-Saffar, Zakaria Y. Algamal
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