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May 9, 2019 · 3.2 Shrinkage estimator for mutual information. By estimating the optimal shrinkage intensity using Eq. (13), we can estimate our novel shrinkage estimates for the probabilities, Eq. (12), and by plugging these probabilities in the MI expression of Eq. (1), we can derive a novel shrinkage estimator for the MI:
- Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin B...
- 2019
Jan 1, 2014 · The optimal shrinkage estimator is similar to the most celebrated J–S estimator in which they shrink the benchmark estimator toward the null vector for estimating the mean vector of a multivariate normal distribution (see Multivariate Normal Distributions). There is no mystery about the origin; these estimators can shrink toward any point.
Fig. 5 The relative losses for the portfolios based in the optimal shrinkage estimator, the traditional estimator and the equally weighted portfolio as a function of the dimension pfor c=0.2 (top left), 0.5 (top right), 0.8 (bottom left), 2 (bottom right). The condition index is set to 1000 and the mean-variance calibration criteria is used.
Nov 8, 2022 · A direct approach to turning the optimal shrinkage estimator into an available one is the plug-in strategy, which replaces the unknown scalar quantities with their estimates 21,22,23.
Aug 1, 2013 · The key then is to characterize the optimal trade-off between the sample estimator (low bias), and the target (low variance). In other words, shrinkage estimators can help reduce estimation error, but the shrinkage intensity (size) matters. In this paper, we make an extensive investigation of shrinkage estimators for portfolio selection.
- Victor DeMiguel, Alberto Martin-Utrera, Francisco J. Nogales
- 2013
Jan 1, 2014 · However, the key question in this type of estimator is how to select an optimal value for the shrinkage parameter π. In some situations, it may suffice to fix the parameter at some given value. The second choice, is to choose the parameter in a data-driven fashion by explicitly minimizing a suitable risk function.
Jul 18, 2022 · That is the class of linear shrinkage estimators is simply all estimators of the form (1) when a varies. Nonlinear shrinkage derives an asymptotic estimator in a much larger class (that need not be just a linear function of the scm). In particular, it solves the problem. where l is some loss function.
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