Results 11 to 20 of about 25,682 (291)
Kernel Mean Shrinkage Estimators [PDF]
, 2016 A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principal component analysis, and it also forms the core inference step of modern ...
Fukumizu, Kenji +4 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 ...
Agrawal Shipra +6 more
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Shrinkage Estimation in Multilevel Normal Models
Statistical Science, 2012 This review traces the evolution of theory that started when Charles Stein in 1955 [In Proc. 3rd Berkeley Sympos. Math. Statist. Probab. I (1956) 197--206, Univ.
Lysy, Martin, Morris, Carl N.
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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 MJ, Kass RE.
europepmc +5 more sources
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 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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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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