Results 21 to 30 of about 3,804 (260)
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 +5 more
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Improved estimators for the rate parameter of gamma model using asymptotic properties
Heliyon, 2021 In this paper we proposed three estimators namely linear shrinkage, preliminary test and shrinkage preliminary test for the rate parameter of univariate gamma.
Nana Kena Frempong +3 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 +2 more
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Composition Estimation Via Shrinkage
SSRN Electronic Journal, 2022 In this note, we explore a simple approach to composition estimation, using penalized likelihood density estimation on a nominal discrete domain. Practical issues such as smoothing parameter selection and the use of prior information are investigated in simulations, and a theoretical analysis is attempted. The method has been implemented in a pair of R
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Double-Stage Shrinkage Estimation of Reliability Function for Burr XII Distribution
Iraqi Journal for Computer Science and Mathematics, 2022 This study is concerned with the problem of estimating the reliability function of the parameters of the two-parameter Burr XII distribution when the data are complete.
Iman Jalil Atewi +2 more
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Estimating the Variance of an Exponential Distribution in the Presence of Large True Observations
Austrian Journal of Statistics, 2016 The present paper discusses some classes of shrinkage estimators for the variance of the exponential distribution in the presence of large true observations when some a priori or guessed interval containing the variance parameter is available from some ...
Housila P. Singh, Vankim Chander
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On Improved Loss Estimation for Shrinkage Estimators
Statistical Science, 2012 Let $X$ be a random vector with distribution $P_θ$ where $θ$ is an unknown parameter. When estimating $θ$ by some estimator $φ(X)$ under a loss function $L(θ,φ)$, classical decision theory advocates that such a decision rule should be used if it has suitable properties with respect to the frequentist risk $R(θ,φ)$. However, after having observed $X=x$,
Fourdrinier, Dominique, Wells, Martin T.
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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 Approaches to Shrinkage and Sparse Estimation [PDF]
Foundations and Trends® in Econometrics, 2021 In all areas of human knowledge, datasets are increasing in both size and complexity, creating the need for richer statistical models. This trend is also true for economic data, where high-dimensional and nonlinear/nonparametric inference is the norm in several fields of applied econometric work. The purpose of this monograph is to introduce the reader
Korompilis, Dimitris, Shimizu, Kenichi
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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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