4 papers
Quantifying Dependence Between Random Vectors: A New Index with Applications
Chuancun yin
This article proposes a new index for quantifying the degree of dependence between random vectors. The index takes values in [0,1] and equals zero if and only if the random vectors…
Analyzing distortion riskmetrics and weighted entropy for unimodal and symmetric distributions under partial information constraints
Baishuai Zuo, Chuancun Yin
In this paper, we develop the lower and upper bounds of worst-case distortion riskmetrics and weighted entropy for unimodal, and symmetric unimodal distributions when mean and vari…
Best- and worst-case Scenarios for GlueVaR distortion risk measure with Incomplete information
Mengshuo Zhao, Chuancun Yin
This paper derives the best- and worst-case GlueVaR distortion risk measure within a unified framework, based on partial information of the underlying distributions and shape infor…
Worst-cases of distortion riskmetrics and weighted entropy with partial information
Baishuai Zuo, Chuancun Yin
In this paper, we discuss the worst-case of distortion riskmetrics for general distributions when only partial information (mean and variance) is known. This result is applicable t…