失真风险度量、ROC曲线和失真偏差

IF 1.3 Q2 STATISTICS & PROBABILITY

Statistics & Risk Modeling Pub Date : 2017-10-18 DOI:10.1515/strm-2017-0012

J. Schumacher

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引用次数: 0

摘要

摘要失真函数用于定义风险度量。受试者工作特性（ROC）曲线用于描述参数化测试家族在测试简单零假设与简单替代方案时的性能。本文一方面提供了畸变函数和ROC曲线之间的联系。这导致了对一些众所周知的失真风险度量类别的新解释，以及概率度量之间分歧的新概念。

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Distortion risk measures, ROC curves, and distortion divergence

Abstract Distortion functions are employed to define measures of risk. Receiver operating characteristic (ROC) curves are used to describe the performance of parametrized test families in testing a simple null hypothesis against a simple alternative. This paper provides a connection between distortion functions on the one hand, and ROC curves on the other. This leads to a new interpretation of some well-known classes of distortion risk measures, and to a new notion of divergence between probability measures.

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来源期刊

Statistics & Risk Modeling STATISTICS & PROBABILITY-

CiteScore

1.80

自引率

6.70%

发文量

期刊介绍： Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.