Results 11 to 20 of about 7,690 (301)

Bayesian Shrinkage Estimator of Burr XII Distribution

International Journal of Mathematics and Mathematical Sciences, 2020
In this paper, we derive the generalized Bayesian shrinkage estimator of parameter of Burr XII distribution under three loss functions: squared error, LINEX, and weighted balance loss functions.
N. J. Hassan, J. Mahdi Hadad, A. Hawad Nasar   +2 more
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An analytical shrinkage estimator for linear regression

Statistics & Probability Letters, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lassance, Nathan, Nathan Lassance
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On Jeffery Prior Distribution in Modified Double Stage Shrinkage-Bayesian Estimator for Exponential Mean

Journal of Kufa for Mathematics and Computer, 2012
        This paper is concerned with Modified Double Stage Shrinkage Bayesian (DSSB) Estimator for lowering the mean squared error of classical estimator  for the scale parameter (q) of an Exponential Distribution in suitable region (R) around ...
Abbas Najim Salman, Assel Hussein Ali, Maha A Mohammed   +2 more
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Generalized ridge estimator shrinkage estimation based on particle swarm optimization algorithm [PDF]

المجلة العراقية للعلوم الاحصائية, 2020
It is well-known that in the presence of multicollinearity, the ridge estimator is an alternative to the ordinary least square (OLS) estimator. Generalized ridge estimator (GRE) is an generalization of the ridge estimator.
Qamar Abdul kareem, Zakariya Algamal
doaj   +1 more source

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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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
doaj   +1 more source

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, Eytan Bakshy, Jasjeet S. Sekhon   +2 more
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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., Pascal, Frédéric, Ollila, Esa   +3 more
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On Some Classes of Estimators Derived from the Positive Part of James–Stein Estimator

Journal of Mathematics, 2023
This work consists of developing shrinkage estimation strategies for the multivariate normal mean when the covariance matrix is diagonal and known. The domination of the positive part of James–Stein estimator (PPJSE) over James–Stein estimator (JSE ...
Abdenour Hamdaoui, Abdelkader Benkhaled, Mohammed Alshahrani, Mekki Terbeche, Waleed Almutiry, Amani Alahmadi   +5 more
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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, Nickerson, David M., Sen, Pranab K.   +2 more
openaire   +3 more sources