Results 11 to 20 of about 144,075 (292)
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 ...
Datta, G., Ghosh, M.
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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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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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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 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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Adaptive Robust Efficient Methods for Periodic Signal Processing Observed with Colours Noises
Advances in Electrical and Electronic Engineering, 2019 In this paper, we consider the problem of robust adaptive efficient estimating a periodic signal observed in the transmission channel with the dependent noise defined by non-Gaussian Ornstein-Uhlenbeck processes with unknown correlation properties ...
Evgeny Pchelintsev +2 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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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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