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[Formula: see text]-optimality in copula models.

Elisa Perrone1, Andreas Rappold2, Werner G Müller2

  • 1IST Austria, Am Campus 1, 3400 Klosterneuburg, Austria.

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|May 15, 2018
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Summary
This summary is machine-generated.

This study extends optimum experimental design theory for parameter estimation in flexible copula models. It introduces a novel approach using [Formula: see text]-optimality to address challenges in estimating subsets of model parameters.

Keywords:
Copula selectionDesign discriminationStochastic dependence[Formula: see text]-optimality

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Area of Science:

  • Statistics
  • Probability Theory
  • Econometrics

Background:

  • Copula models offer flexibility by separating marginal and dependence parameters.
  • Estimating only a subset of copula parameters presents a significant challenge.
  • Optimum experimental design is crucial for efficient parameter estimation.

Purpose of the Study:

  • To extend optimum experimental design theory for parameter estimation in copula models.
  • To apply [Formula: see text]-optimality to address the challenge of subset parameter estimation in copulas.
  • To analyze the utility of [Formula: see text]-optimality across various flexible copula models.

Main Methods:

  • Extension of [Formula: see text]-optimality equivalence theory for copula models.
  • Application of the extended theory to a diverse range of flexible copula families.
  • Analysis of scenarios to demonstrate the practical usefulness of the proposed criterion.

Main Results:

  • Successful extension of [Formula: see text]-optimality and its equivalence theory for copula models.
  • Demonstration of the effectiveness of [Formula: see text]-optimality in handling subset parameter estimation.
  • Highlighting the broad applicability of [Formula: see text]-optimality across different copula structures.

Conclusions:

  • The proposed [Formula: see text]-optimality criterion is a valuable tool for parameter estimation in copula models.
  • This approach enhances the flexibility and efficiency of statistical modeling with copulas.
  • The study provides insights into copula selection and optimal design for model discrimination.