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Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator [PDF]
Operations Research, 2022 Note. The best result in each experiment is highlighted in bold.The optimal solutions of many decision problems such as the Markowitz portfolio allocation and the linear discriminant analysis depend on the inverse covariance matrix of a Gaussian random vector.
Nguyen, Viet Anh +2 more
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Comparison of Risk Ratios of Shrinkage Estimators in High Dimensions
Mathematics, 2021 In this paper, we analyze the risk ratios of several shrinkage estimators using a balanced loss function. The James–Stein estimator is one of a group of shrinkage estimators that has been proposed in the existing literature.
Abdenour Hamdaoui +3 more
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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 estimators in inverse Gaussian regression model: Subject review [PDF]
المجلة العراقية للعلوم الاحصائية, 2022 The presence of the high correlation among predictors in regression modeling has undesirable effects on the regression estimating. There are several available biased methods to overcome this issue.
Farah Abd ulghani +1 more
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Partial Coherence Estimation via Spectral Matrix Shrinkage under Quadratic Loss [PDF]
, 2015 Partial coherence is an important quantity derived from spectral or precision matrices and is used in seismology, meteorology, oceanography, neuroscience and elsewhere.
Schneider-Luftman, D., Walden, A. T.
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Frontiers in Forests and Global Change, 2021 Forest information is requested at many levels and for many purposes. Sampling-based national forest inventories (NFIs) can provide reliable estimates on national and regional levels.
Magnus Ekström +2 more
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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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StatisticaShrinkage methods for estimating the parameters of a regression model with autoregressive integrated moving average (ARIMA) errors are presented when some of the regression parameters are restricted to a subspace.
Sharandeep Pandher +2 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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