Results 21 to 30 of about 25,719 (290)
Shrinkage Estimators for the Intercept in Linear and Uplift Regression
Scientific Annals of Computer Science, 2023 Shrinkage estimators modify classical statistical estimators by scaling them towards zero in order to decrease their prediction error. We propose shrinkage estimators for linear regression models which explicitly take into account the presence ...
Szymon Jaroszewicz, Krzysztof Rudas
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Shrinkage Estimation of Linear Regression Models with GARCH Errors [PDF]
Journal of Statistical Theory and Applications (JSTA), 2016 This paper introduces shrinkage estimators for the parameter vector of a linear regression model with con- ditionally heteroscedastic errors such as the class of generalized autoregressive conditional heteroscedastic (GARCH) errors when some of the ...
S. Hossain, M. Ghahramani
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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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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 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 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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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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