Results 11 to 20 of about 25,719 (290)
Shrinkage estimators of large covariance matrices with Toeplitz targets in array signal processing [PDF]
Scientific Reports, 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.
Bin Zhang, Shoucheng Yuan
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Improving reliability of subject-level resting-state fMRI parcellation with shrinkage estimators. [PDF]
Neuroimage, 2015 A recent interest in resting state functional magnetic resonance imaging (rsfMRI) lies in subdividing the human brain into anatomically and functionally distinct regions of interest.
Mejia AF +7 more
europepmc +3 more sources
Enhancing accuracy in modelling highly multicollinear data using alternative shrinkage parameters for ridge regression methods [PDF]
Scientific ReportsIn 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.
Nadeem Akhtar, Muteb Faraj Alharthi
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Kernel Mean Shrinkage Estimators [PDF]
, 2016 A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principal component analysis, and it also forms the core inference step of modern ...
Fukumizu, Kenji +4 more
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Shrinkage Estimators for Reliability Function [PDF]
مجلة جامعة النجاح للأبحاث العلوم الطبيعية, 2012 A variety of shrinkage methods for estimating unknown parameters has been considered. We derive and compare the shrinkage estimators for the reliability function of the two-parameter exponential distribution.
Mohammad Qabaha
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Shrinkage Estimators in Online Experiments [PDF]
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019 We develop and analyze empirical Bayes Stein-type estimators for use in the estimation of causal effects in large-scale online experiments. While online experiments are generally thought to be distinguished by their large sample size, we focus on the ...
Agrawal Shipra +6 more
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Shrinkage estimators for covariance matrices. [PDF]
Biometrics, 2001 Estimation of covariance matrices in small samples has been studied by many authors. Standard estimators, like the unstructured maximum likelihood estimator (ML) or restricted maximum likelihood (REML) estimator, can be very unstable with the smallest estimated eigenvalues being too small and the largest too big.
Daniels MJ, Kass RE.
europepmc +5 more sources
On Improved Loss Estimation for Shrinkage Estimators
Statistical Science, 2012 Let $X$ be a random vector with distribution $P_{\theta}$ where $\theta$ is an unknown parameter. When estimating $\theta$ by some estimator $\varphi(X)$ under a loss function $L(\theta,\varphi)$, classical decision theory advocates that such a decision ...
Fourdrinier, Dominique, Wells, Martin T.
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Shrinkage Estimation in Multilevel Normal Models
Statistical Science, 2012 This review traces the evolution of theory that started when Charles Stein in 1955 [In Proc. 3rd Berkeley Sympos. Math. Statist. Probab. I (1956) 197--206, Univ.
Lysy, Martin, Morris, Carl N.
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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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