Compact composition operators on the Smirnov class

We show that a composition operator on the Smirnov class is compact if and only if it is compact on some (equivalently: every) Hardy space for . Along the way we show that for composition operators on both the formally weaker notion of boundedness, and a formally stronger notion we call metric compactness, are equivalent to compactness.

     

Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market

We propose a way of using DEA cross-efficiency evaluation in portfolio selection. While cross efficiency is an approach developed for peer evaluation, we improve its use in portfolio selection. In addition to (average) cross-efficiency scores, we suggest to examine the variations of cross-efficiencies, and to incorporate two statistics of cross-efficiencies into the mean-variance formulation of portfolio selection. Two benefits are attained by our proposed approach. One is selection of portfolios well-diversified in terms of their performance on multiple evaluation criteria, and the other is alleviation of the so-called “ganging together” phenomenon of DEA cross-efficiency evaluation in portfolio selection. We apply the proposed approach to stock portfolio selection in the Korean stock market, and demonstrate that the proposed approach can be a promising tool for stock portfolio selection by showing that the selected portfolio yields higher risk-adjusted returns than other benchmark portfolios for a 9-year sample period from 2002 to 2011.     

GA-Ensemble: a genetic algorithm for robust ensembles

Many simple and complex methods have been developed to solve the classification problem. Boosting is one of the best known techniques for improving the accuracy of classifiers. However, boosting is prone to overfitting with noisy data and the final model is difficult to interpret. Some boosting methods, including AdaBoost, are also very sensitive to outliers. In this article we propose a new method, GA-Ensemble, which directly solves for the set of weak classifiers and their associated weights using a genetic algorithm. The genetic algorithm utilizes a new penalized fitness function that limits the number of weak classifiers and controls the effects of outliers by maximizing an appropriately chosen $p$ th percentile of margins. We compare the test set error rates of GA-Ensemble, AdaBoost, and GentleBoost (an outlier-resistant version of AdaBoost) using several artificial data sets and real-world data sets from the UC-Irvine Machine Learning Repository. GA-Ensemble is found to be more resistant to outliers and results in simpler predictive models than AdaBoost and GentleBoost.     

Asymptotic Stability of Harmonic Maps on the Hyperbolic Plane under the Schrödinger Maps Evolution

We consider the Cauchy problem for the Schrödinger maps evolution when the domain is the hyperbolic plane. An interesting feature of this problem compared to the more widely studied case on the Euclidean plane is the existence of a rich new family of finite energy harmonic maps. These are stationary solutions, and thus play an important role in the dynamics of Schrödinger maps. The main result of this article is the asymptotic stability of (some of) such harmonic maps under the Schrödinger maps evolution. More precisely, we prove the nonlinear asymptotic stability of a finite energy equivariant harmonic map under the Schrödinger maps evolution with respect to non-equivariant perturbations, provided obeys a suitable linearized stability condition. This condition is known to hold for all equivariant harmonic maps with values in the hyperbolic plane and for a subset of those maps taking values in the sphere. One of the main technical ingredients in the paper is a global-in-time local smoothing and Strichartz estimate for the operator obtained by linearization around a harmonic map, proved in the companion paper [36]. © 2021 Wiley Periodicals LLC.