University of Geneva

About: University of Geneva is a education organization based out in Geneva, Switzerland. It is known for research contribution in the topics: Population & Planet. The organization has 26887 authors who have published 65265 publications receiving 2931373 citations. The organization is also known as: Geneva University & Universite de Geneve.

False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas.

Abstract: Standard tests designed to identify mutual funds with non-zero alphas are problematic, in that they do not adequately account for the presence of lucky funds. Lucky funds have significant estimated alphas, while their true alphas are equal to zero. To address this issue, this paper quantifies the impact of luck with new measures built on the False Discovery Rate (FDR). These FDR measures provide a simple way to compute the proportion of funds with genuine positive or negative performance as well as their location in the cross-sectional alpha distribution. Using a large cross-section of U.S. domestic-equity funds, we find that about one fifth of the funds in the population truly yield negative alphas. These funds are dispersed in the left tail of the alpha distribution. We also find a small proportion of funds with truly positive performance, which are concentrated in the extreme right tail of the alpha distribution.

Stability of feature selection algorithms: a study on high-dimensional spaces

Abstract: With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms Finally, we show how stability profiles can support the choice of a feature selection algorithm

Correlated gene expression supports synchronous activity in brain networks

Abstract: During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function.