Results 11 to 20 of about 3,804 (260)
Shrinkage estimators of large covariance matrices with Toeplitz targets in array signal processing. [PDF]
Sci Rep, 2022 The problem of estimating a large covariance matrix arises in various statistical applications. This paper develops new covariance matrix estimators based on shrinkage regularization.
Zhang B, Yuan S.
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A Class of Shrinkage Estimators
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1978 Summary In this paper we define a class of shrinkage estimators, all of whose members have a mean square error matrix which is less than that of the ordinary least squares estimator by a positive semidefinite matrix if (β-b *)T X T X (β-b *) ≤ σ2.
R W Farebrother
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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).
Abdelkader Benkhaled +2 more
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IEEE Access, 2019 Shrinkage regularization is an effective strategy to estimate the covariance matrix of multi-variate random vector in small sample scenarios. The purpose of this paper is to propose improved linear shrinkage estimators of covariance matrix as two types ...
Bin Zhang, Jianbo Li
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Enhancing accuracy in modelling highly multicollinear data using alternative shrinkage parameters for ridge regression methods. [PDF]
Sci RepIn this study, we introduce three new shrinkage parameters for ridge regression, which dynamically adjust the ridge penalty based on the properties of the data, particularly the multicollinearity structure.
Akhtar N, Alharthi MF.
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On shrinkage estimators improving the positive part of James-Stein estimator
Demonstratio Mathematica, 2021 In this work, we study the estimation of the multivariate normal mean by different classes of shrinkage estimators. The risk associated with the quadratic loss function is used to compare two estimators. We start by considering a class of estimators that
Abdenour Hamdaoui
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Linear Shrinkage and Shrinkage Pretest Strategies in Partially Linear Models [PDF]
E3S Web of Conferences, 2023 In this paper, we improved the efficiency of parameter estimation in partially linear models, where subspace information is available. We proposed linear shrinkage and shrinkage pretest estimation strategies.
Phukongtong Siwaporn +2 more
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Shrinkage Estimators for the Intercept in Linear and Uplift Regression
Scientific Annals of Computer Science, 2023 Shrinkage estimators modify classical statistical estimators by scaling them towards zero in order to decrease their prediction error. We propose shrinkage estimators for linear regression models which explicitly take into account the presence ...
Szymon Jaroszewicz, Krzysztof Rudas
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Data envelopment analysis with shrinkage estimators
OR SpectrumAbstract Shrinkage estimators reduce estimation risk in multivariate statistics such as mean and standard deviation. They have not been used before in data envelopment analysis (DEA). By considering models of investment fund returns, we show that estimation risk can cause the range of estimates of inputs and outputs in a DEA model to ...
John D Lamb, Kai-Hong Tee
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Shrinkage Estimation of Linear Regression Models with GARCH Errors [PDF]
Journal of Statistical Theory and Applications (JSTA), 2016 This paper introduces shrinkage estimators for the parameter vector of a linear regression model with con- ditionally heteroscedastic errors such as the class of generalized autoregressive conditional heteroscedastic (GARCH) errors when some of the ...
S. Hossain, M. Ghahramani
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