For multivariate distributions in the domain of attraction a max-stable distribution, tail copula and stable dependence function are equivalent ways to capture upper tail. The empirical versions these functions rank-based estimators whose inflated estimation errors known converge weakly Gaussian process that is similar structure weak limit process. We extend this result continuous functional da...

Abstract According to contextualism, the extension of claims personal taste is dependent on context utterance. truth relativism, their depends assessment. On this view, when preferences a speaker change, so does value previously uttered claim, and might be required retract it. Both views make strong empirical assumptions, which are here put test in three experiments with over 740 participants. ...

This paper surveys some of the quantitative empirical research in two areas of Marxist political economy: (a) Marxist national accounts, and (b) Marxist responses to the Sraffa-based critique of the 1970s. With respect to the first area, this paper explains the basic methodology underlying the construction of Marxist national accounts from traditional input-output data. With respect to the seco...

This essay identifies the empirical facts about lobbying which are generally agreed upon in the literature. It then discusses challenges to empirical research in lobbying and provides examples of empirical methods that can be employed to overcome these challenges—with an emphasis on statistical measurement, identification, and casual inference. The essay then discusses the advantages, disadvant...

We introduce a novel model for spatially varying variational data fusion, driven by point-wise confidence values. The proposed model allows for the joint estimation of the data and the confidence values based on the spatial coherence of the data. We discuss the main properties of the introduced model as well as suitable algorithms for estimating the solution of the corresponding biconvex minimi...

In many important machine learning applications, the source distribution used to estimate a probabilistic classifier differs from the target distribution on which the classifier will be used to make predictions. Due to its asymptotic properties, sample reweighted empirical loss minimization is a commonly employed technique to deal with this difference. However, given finite amounts of labeled s...

We study loss functions that measure the accuracy of a prediction based on multiple data points simultaneously. To our knowledge, such loss functions have not been studied before in the area of property elicitation or in machine learning more broadly. As compared to traditional loss functions that take only a single data point, these multi-observation loss functions can in some cases drasticall...