On a strategy to develop robust and simple tariffs from motor vehicle insurance data

The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. The strategy is applied to a data set from motor vehicle insurance companies. We use a nonparametric approach based on a combination of kernel logistic regression and ¡support vector regression. --Classification,Data Mining,Insurance tariffs,Kernel logistic regression,Machine learning,Regression,Robustness,Simplicity,Support Vector Machine,Support Vector Regression

Regression depth and support vector machine

The regression depth method (RDM) proposed by Rousseeuw and Hubert [RH99] plays an important role in the area of robust regression for a continuous response variable. Christmann and Rousseeuw [CR01] showed that RDM is also useful for the case of binary regression. Vapnik?s convex risk minimization principle [Vap98] has a dominating role in statistical machine learning theory. Important special cases are the support vector machine (SVM), [epsilon]-support vector regression and kernel logistic regression. In this paper connections between these methods from different disciplines are investigated for the case of pattern recognition. Some results concerning the robustness of the SVM and other kernel based methods are given. --

Getting paid for sex is my kick: a qualitative study of male sex workers

As with its female counterpart, male sex work (MSW) has generally been regarded as deeply problematic, either because of negative societal attitudes to the selling of sex or the prevalence of psychosocial and economic problems amongst those attracted to MSW and the attendant health risks and dangers encountered whilst engaged in it. While the phenomenon of female sex work has received a great deal of criminological scrutiny, there has been comparatively less attention paid to male sex workers (MSWs). The research which we report on in this chapter aimed to further our understanding of the motivations of MSWs, the risks they face, their engagement with support agencies and their intentions for the future

On robustness properties of convex risk minimization methods for pattern recognition

The paper brings together methods from two disciplines: machine learning theory and robust statistics. Robustness properties of machine learning methods based on convex risk minimization are investigated for the problem of pattern recognition. Assumptions are given for the existence of the influence function of the classifiers and for bounds of the influence function. Kernel logistic regression, support vector machines, least squares and the AdaBoost loss function are treated as special cases. A sensitivity analysis of the support vector machine is given. --AdaBoost loss function,influence function,kernel logistic regression,robustness,sensitivity curve,statistical learning,support vector machine,total variation

Robust Learning from Bites

Many robust statistical procedures have two drawbacks. Firstly, they are computer-intensive such that they can hardly be used for massive data sets. Secondly, robust confidence intervals for the estimated parameters or robust predictions according to the fitted models are often unknown. Here, we propose a general method to overcome these problems of robust estimation in the context of huge data sets. The method is scalable to the memory of the computer, can be distributed on several processors if available, and can help to reduce the computation time substantially. The method additionally offers distribution-free confidence intervals for the median of the predictions. The method is illustrated for two situations: robust estimation in linear regression and kernel logistic regression from statistical machine learning. --

No judge, no job!: Judicial discretion and incomplete labor contracts

The decision making of judges is prone to error and misapprehension. Consequently, the prevailing literature ties the economic function of courts to dispute resolution and minimization of rule making costs. In contrast to previous research, this analysis applies a contract theoretic perspective to the ruling of courts and keeps the focus on the implemented market transactions. Using labor contracts as institutional setting, performance and limitations of judicial law making are formally investigated and compared to the effects of specific legislation. It is shown that the efficient relation of legislative law making and judicial discretion is defined by the characteristics of the particular field of law and the actual market structure. The model also suggests a mutual dependency between legislation and adjudication to establish efficiency in law, contradicting the traditional legal doctrines of exclusive legislation or sole case-law. --incomplete contracts,judicial law making,legislation