Maximum Entropy Principle with General Deviation Measures
Mathematics of Operations Research
Chebyshev inequalities with law-invariant deviation measures
Probability in the Engineering and Informational Sciences
Conditional Value-at-Risk and Average Value-at-Risk: Estimation and Asymptotics
Operations Research
Entropy Coherent and Entropy Convex Measures of Risk
Mathematics of Operations Research
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A framework is set up in which linear regression, as a way of approximating a random variable by other random variables, can be carried out in a variety of ways, which, moreover, can be tuned to the needs of a particular model in finance, or operations research more broadly. Although the idea of adapting the form of regression to the circumstances at hand has already found advocates in promoting quantile regression as an alternative to classical least-squares approaches, it is carried here much farther than that. Axiomatic concepts of error measure, deviation measure, and risk measure are coordinated with certain “statistics” that likewise say something about a random variable. Problems of regression utilizing these concepts are analyzed and the character of their solutions is explored in a range of examples. Special attention is paid to parametric forms of regression which arise in connection with factor models. It is argued that when different aspects of risk enter an optimization problem, different forms of regression ought to be invoked for each of those aspects.