GENERALIZED CHEBYSHEV INEQUALITIES - THEORY AND APPLICATIONS IN DECISION-ANALYSIS
成果类型：
Article
署名作者：
SMITH, JE
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.43.5.807
发表日期：
1995
页码：
807-825
关键词：
摘要：
In many decision analysis problems, we have only limited information about the relevant probability distributions. In problems like these, it is natural to ask what conclusions can be drawn on the basis of this limited information. For example, in the early stages of analysis of a complex problem, we may have only limited fractile information for the distributions in the problem; what can we say about the optimal strategy or certainty equivalents given these few fractiles? This paper describes a very general framework for analyzing these kinds of problems where, given certain ''moments'' of a distribution, we can compute bounds on the expected value of an arbitrary ''objective'' function. By suitable choice of moment and objective functions we can formulate and solve many practical decision analysis problems. We describe the general framework and theoretical results, discuss computational strategies, and provide specific results for examples in dynamic programming, decision analysis with incomplete information, Bayesian statistics, and option pricing.