It is a well known fact that at high sampling frequencies, the contamination of microstructure noise causes the Realized Variance to be a biased measure of the Integrated Variance. Recent developments in this field propose sampling on lower frequencies, sub-sampling techniques, or bias corrections using the autocorrelation patterns in the data. In this paper we propose a structural decompositio...

This paper presents a unified bias-variance decomposition that is applicable to squared loss, zero-one loss, variable misclassification costs, and other loss functions. The unified decomposition sheds light on a number of significant issues: the relation between some of the previously-proposed decompositions for zero-one loss and the original one for squared loss, the relation between bias, var...

Abstract I n this paper, the behavior of the real oil price and OPEC and non-OPEC oil production during 1973-2013 are modelled. Interactions among OPEC, non-OPEC oil production, global oil consumption, and the real price of crude oil are estimated using a Structural VAR model (SVAR). After providing evidence for the structural breaks in oil price in 1996, the results indicate that, ac...

Bias-variance analysis provides a tool to study learning algorithms and can be used to properly design ensemble methods well tuned to the properties of a specific base learner. Indeed the effectiveness of ensemble methods critically depends on accuracy, diversity and learning characteristics of base learners. We present an extended experimental analysis of bias-variance decomposition of the err...

Total variance is a statistical tool developed for improved estimates of frequency stability at averaging times up to half the test duration. As a descriptive statistic, Total variance performs an exact decomposition of the sample variance of the frequency residuals into components associated with descending frequency octaves. As an estimator of Allan variance, Total variance has modest bias an...

The so-called “smooth orthogonal decomposition” technique, developed in the nonlinear vibrations and fatigue community, is applied to an oceanographic data set. This decomposition technique overcomes some limitations of the proper orthogonal decomposition technique by identifying modes which behave smoothly in time and thus being sensitive to both variance amplitude as well as frequency. This i...