The contribution of this paper is the adaptation of data driven methods for non-Euclidean metric decomposition of tangent space shape coordinates. The basic idea is to extend principal component analysis (PCA) to take into account the noise variance at different landmarks and at different shapes. We show examples where these non-Euclidean metric methods allow for easier interpretation by decomp...

The bias-variance decomposition is a very useful and widely-used tool for understanding machine-learning algorithms. It was originally developed for squared loss. In recent years, several authors have proposed decompositions for zero-one loss, but each has significant shortcomings. In particular, all of these decompositions have only an intuitive relationship to the original squared-loss one. I...

This paper focuses on the variance introduced by the dis-cretization techniques used to handle continuous attributes in decision tree induction. Diierent discretization procedures are rst studied empirically , then means to reduce the discretization variance are proposed. The experiment shows that discretization variance is large and that it is possible to reduce it signiicantly without notable...

Andrew Gelman’s contribution shifts the focus of “Analysis of Variance” (ANOVA) from the limited sense in which it has been commonly used in classical statistics, as a method of testing, to the broader framework of estimation and inference. The term more commonly used in this sense, “variance components modeling,” also captures the same spirit. The essential idea is that of constructing distrib...

This paper reviews the variance-gamma asset price model as well as its symmetric and non-symmetric extensions based on generalized gamma convolutions (GGC). In particular we compute the basic characteristics and decomposition of the variance-GGC model, and we consider its sensitivity analysis based on the approach of [8].