Results 31 to 40 of about 144,075 (292)
From Minimax Shrinkage Estimation to Minimax Shrinkage Prediction
Statistical Science, 2012 Published in at http://dx.doi.org/10.1214/11-STS383 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
George, Edward I, Liang, Feng, Xu, Xinyi
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Shrinkage estimator for exponential smoothing models
International Journal of Forecasting, 2023 Exponential smoothing is widely used in practice and has shown its efficacy and reliability in many business applications. Yet there are cases, for example when the estimation sample is limited, where the estimated smoothing parameters can be erroneous, often unnecessarily large.
Pritularga, Kandrika +2 more
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Shrinkage Estimation Methods for Subgroup Analyses
Statistics in Biopharmaceutical Research, 2022 Subgroup analyses increasingly gain importance for pharmaceutical investigations. Conventional approaches for treatment effect estimation are controversial because of multiplicity and small sample sizes within the subsets. Hence, we consider shrinkage estimators, which combine the overall effect estimate with the estimate within a given subgroup by ...
Riehl, Julian +2 more
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Objective Bayesian Estimators for the Right Censored Rayleigh Distribution
Revstat Statistical Journal, 2016 The Rayleigh distribution, serving as a special case of the Weibull distribution, is known to have wide applications in survival analysis, reliability theory and communication engineering.
J.T. Ferreira , A. Bekker , M. Arashi
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Shrinkage Function And Its Applications In Matrix Approximation
, 2017 The shrinkage function is widely used in matrix low-rank approximation, compressive sensing, and statistical estimation. In this article, an elementary derivation of the shrinkage function is given. In addition, applications of the shrinkage function are
Boas, Toby +4 more
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Nonparametric Stein-type Shrinkage Covariance Matrix Estimators in High-Dimensional Settings [PDF]
, 2014 Estimating a covariance matrix is an important task in applications where the number of variables is larger than the number of observations. Shrinkage approaches for estimating a high-dimensional covariance matrix are often employed to circumvent the ...
Touloumis, Anestis
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Shrinkage Algorithms for MMSE Covariance Estimation
, 2009 We address covariance estimation in the sense of minimum mean-squared error (MMSE) for Gaussian samples. Specifically, we consider shrinkage methods which are suitable for high dimensional problems with a small number of samples (large p small n). First,
Alfred O. Hero +5 more
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Efficient feature selection using shrinkage estimators [PDF]
Machine Learning, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Konstantinos Sechidis +5 more
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A nonparametric mean-variance smoothing method to assess Arabidopsis cold stress transcriptional regulator CBF2 overexpression microarray data. [PDF]
PLoS ONE, 2011 Microarray is a powerful tool for genome-wide gene expression analysis. In microarray expression data, often mean and variance have certain relationships.
Pingsha Hu, Tapabrata Maiti
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On Improved Loss Estimation for Shrinkage Estimators
Statistical Science, 2012 Let $X$ be a random vector with distribution $P_ $ where $ $ is an unknown parameter. When estimating $ $ by some estimator $ (X)$ under a loss function $L( , )$, classical decision theory advocates that such a decision rule should be used if it has suitable properties with respect to the frequentist risk $R( , )$.
Fourdrinier, Dominique, Wells, Martin T.
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