Results 11 to 20 of about 34,245 (290)
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
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PROPOSED SHRINKAGE FUNCTION FOR A SINGLE OBSERVATION IN N(Θ,1) PROBLEM [PDF]
مجلة جامعة الانبار للعلوم الصرفة, 2012 In this search, Shrinkage Function has been suggested for a Single Observation in N(θ,1) problem. We proved that there is a relationship between Shrinkage Estimator and Normal Bayes Estimator. properties of Proposed Estimator, optimal case, properties of
AMER FADHEL 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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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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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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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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Designing experiments toward shrinkage estimation
Electronic Journal of Statistics, 2023 We consider how increasingly available observational data can be used to improve the design of randomized controlled trials (RCTs). We seek to design a prospective RCT, with the intent of using an Empirical Bayes estimator to shrink the causal estimates from our trial toward causal estimates obtained from an observational study.
Rosenman, Evan T. R., Miratrix, Luke
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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 +5 more
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Generalized robust shrinkage estimator and its application to STAP detection problem [PDF]
, 2014 Recently, in the context of covariance matrix estimation, in order to improve as well as to regularize the performance of the Tyler's estimator [1] also called the Fixed-Point Estimator (FPE) [2], a "shrinkage" fixed-point estimator has been introduced ...
Chitour, Yacine +2 more
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Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models [PDF]
, 2020 Penalization of the likelihood by Jeffreys' invariant prior, or by a positive power thereof, is shown to produce finite-valued maximum penalized likelihood estimates in a broad class of binomial generalized linear models.
Firth, David, Kosmidis, Ioannis
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