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Shrinkage Estimation

, 2018
This book provides a coherent framework for understanding shrinkage estimation in statistics. The term refers to modifying a classical estimator by moving it closer to a target which could be known a priori or arise from a model. The goal is to construct
Dominique Fourdrinier, William E. Strawderman, Martin T. Wells   +2 more
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Interval Shrinkage Estimators

SSRN Electronic Journal, 2010
This paper considers estimation of an unknown distribution parameter in situations where we believe that the parameter belongs to a finite interval. We propose for such situations an interval shrinkage approach which combines in a coherent way an unbiased conventional estimator and non-sample information about the range of plausible parameter values ...
Vasyl Golosnoy, Roman Liesenfeld
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Effective Memory Shrinkage in Estimation

2018 IEEE International Symposium on Information Theory (ISIT), 2018
It is known that a processor with limited memory consisting of an m-state machine can distinguish two coins with biases that differ by $1/m$ . On the other hand, the best additive accuracy with which the same processor can estimate the bias of a coin is only $1/\sqrt{m}$ .
Ayush Jain 0001, Himanshu Tyagi
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Statistical estimation for hyper shrinkage

Digital Signal Processing, 2007
A new shrinkage technique in wavelet domain called hyper shrinkage that uses hyperbolic function for improved denoising is explained. The methodology is statistically significant in terms of signal recovery and improving signal-to-noise ratio over both hard and soft shrinkage. A mathematical treatment of proposed shrinkage function shows an improvement
S. Poornachandra, Natesan Kumaravel
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Shrinkage Estimators for Uplift Regression

2020
Uplift modeling is an approach to machine learning which allows for predicting the net effect of an action (with respect to not taking the action). To achieve this, the training population is divided into two parts: the treatment group, which is subjected to the action, and the control group, on which the action is not taken. Our task is to construct a
Krzysztof Rudas, Szymon Jaroszewicz
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A Shrinkage Estimator for Combination of Bioassays

Acta Mathematicae Applicatae Sinica, English Series, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiong, Jian, Chen, D. G., Yang, Zhen-Hai
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Prediction with shrinkage estimators

Series Statistics, 1978
It is demonstrated that the prediction mean square error for a general prediction design matrix may be reduced by using one of a general class of shrinkage estimators instead of the least squares estimator.Further, a general characterization is given of those situations in which the potential reduction in prediction mean square error is large.
Goldstein, M., Brown, P. J.
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A volumetric approach to biased estimation: Demonstration on shrinkage estimators

2016 IEEE International Conference on Digital Signal Processing (DSP), 2016
This work proposes a new approach, named as the volumetric design (VD), of developing biased estimators of deterministic parameters that are known in advance to belong to a compact subset in the parameter space. For analytical tractability, this approach is demonstrated on the choice of the shrinkage parameter of an estimator that scales the celebrated
Bikcora, C., Weiland, S.
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ON SHRINKAGE TOWARDS AN ARBITRARY ESTIMATOR

Statistics & Risk Modeling, 1991
For estimating the unknown mean \(\theta \in R^ p\) of a multinormal distribution with independent components and common variance \(\sigma^ 2\) several minimax estimators with respect to quadratic loss are known. The author considers a class of spherically symmetric estimators. These are an adaptive linear combination of the identity estimator X and of
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On the shrinkage of local linear curve estimators

Statistics and Computing, 1997
Local linear curve estimators are typically constructed using a compactly supported kernel, which minimizes edge effects and (in the case of the Epanechnikov kernel) optimizes asymptotic performance in a mean square sense. The use of compactly supported kernels can produce numerical problems, however.
Cheng, Ming-Yen, Hall, Peter, Titterington, D.M.   +2 more
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