Certainty equivalents for three-point discrete-distribution approximations
Management Science
An application of copulas to accident precursor analysis
Management Science
Correlations and Copulas for Decision and Risk Analysis
Management Science
Making Hard Decisions with Decisiontools Suite
Making Hard Decisions with Decisiontools Suite
Initialization for NORTA: Generation of Random Vectors with Specified Marginals and Correlations
INFORMS Journal on Computing
Assessing Dependence: Some Experimental Results
Management Science
Fitting Time-Series Input Processes for Simulation
Operations Research
Using Binomial Decision Trees to Solve Real-Option Valuation Problems
Decision Analysis
Response to Comments on Brandão et al. (2005)
Decision Analysis
Optimal Sequential Exploration: A Binary Learning Model
Decision Analysis
Copula-Based Multivariate Input Models for Stochastic Simulation
Operations Research
Valuing Multifactor Real Options Using an Implied Binomial Tree
Decision Analysis
Generalized Diagonal Band Copulas with Two-Sided Generating Densities
Decision Analysis
A Simulation-Based Approach to Decision Making with Partial Information
Decision Analysis
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This paper presents a general framework based on copulas for modeling dependent multivariate uncertainties through the use of a decision tree. The proposed dependent decision tree model allows multiple dependent uncertainties with arbitrary marginal distributions to be represented in a decision tree with a sequence of conditional probability distributions. This general framework could be naturally applied in decision analysis and real options valuations, as well as in more general applications of dependent probability trees. While this approach to modeling dependencies can be based on several popular copula families as we illustrate, we focus on the use of the normal copula and present an efficient computational method for multivariate decision and risk analysis that can be standardized for convenient application.