Results 21 to 30 of about 41,085 (210)
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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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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meta.shrinkage: An R Package for Meta-Analyses for Simultaneously Estimating Individual Means
Algorithms, 2022 Meta-analysis is an indispensable tool for synthesizing statistical results obtained from individual studies. Recently, non-Bayesian estimators for individual means were proposed by applying three methods: the James–Stein (JS) shrinkage estimator ...
Nanami Taketomi +3 more
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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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From Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM)
Frontiers in Psychology, 2021 This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities (i.e., priors) in addition to observed data.
Michael J. Zyphur +11 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 multiplicity of treatment groups.
Drew Dimmery +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. +3 more
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Shrinkage Estimators of the Reliability Characteristics of a Family of Lifetime Distributions
Statistica, 2016 A family of distributions is considered, which covers many lifetime distributions as specific cases. Two measures of reliability are considered, R(t) = P(X>t) and P = P(X>Y).
Ajit Chaturvedi, Shruti Nandchahal
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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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