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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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Ordinary and Bayesian Shrinkage Estimation [PDF]
مجلة جامعة النجاح للأبحاث العلوم الطبيعية, 2007 In this paper a variety of shrinkage methods for estimating unknown population parameters has been considered. Aprior distribution for the parameters around their natural origins has been postulated and the ordinary Bayes estimators are used in place of ...
Mohammad Qabaha
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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, Michael J., Kass, Robert E.
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On Restricted Shrinkage Jackknife Biased Estimator for Restricted Linear Regression Model [PDF]
مجلة جامعة الانبار للعلوم الصرفة, 2023 In restricted linear regression model, more methods proposed to address the Multicollinearity problem and the high variance. For example, shrinkage biased estimation and optimization (Lagrange function).
Ahmed Mohammed, Feras Algareri
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SHRINKAGE ESTIMATOR FOR A SINGLE OBSERVATION IN N(Θ,V) PROBLEM WITH UNKNOWN VARIANCE [PDF]
مجلة جامعة الانبار للعلوم الصرفة, 2012 this search, Shrinkage Estimator has been studied for a Single Observation in N(θ,V) problem when variance is unknown. We proved that there is a relationship between Shrinkage Estimator and Normal Bayes Estimator.
AMER F. NASSAR
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Cluster-seeking shrinkage estimators [PDF]
2016 IEEE International Symposium on Information Theory (ISIT), 2016 This paper considers the problem of estimating a high-dimensional vector θ ∈ ℝn from a noisy one-time observation. The noise vector is assumed to be i.i.d. Gaussian with known variance. For the squared-error loss function, the James-Stein (JS) estimator is known to dominate the simple maximum-likelihood (ML) estimator when the dimension n exceeds two ...
Koteshwar Srinath, P, Venkataramanan, R
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Modified Liu estimators in the linear regression model: An application to Tobacco data.
PLoS ONE, 2021 BackgroundThe problem of multicollinearity in multiple linear regression models arises when the predictor variables are correlated among each other. The variance of the ordinary least squared estimator become unstable in such situation.
Iqra Babar +5 more
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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 multiplicity of treatment groups.
Drew Dimmery +2 more
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Shrinkage estimation of the regression parameters with multivariate normal errors [PDF]
Iranian Journal of Numerical Analysis and Optimization, 2008 In the linear model y=XB+e with the errors distributed as normal, we obtain generalized least square (GLS), restricted GLS (RGLS), preliminary test (PT), Stein-type shrinkage (S) and positive-rule shrinkage (PRS) estimators for regression vector ...
M. Arashi, S. M. M. Tabatabaey
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M-Estimators of Scatter with Eigenvalue Shrinkage [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 A popular regularized (shrinkage) covariance estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward its grand mean. In this paper, a more general approach is considered in which the SCM is replaced by an M-estimator of scatter matrix and a fully automatic data ...
Palomar, Daniel P. +3 more
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