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  1. Home
  3. Copula Identifiability Conditions For Dependent Truncated Data Model.
  1. Home
  3. Copula Identifiability Conditions For Dependent Truncated Data Model.

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Copula identifiability conditions for dependent truncated data model.

A Adam Ding1

  • 1Department of Mathematics, Northeastern University, Boston, MA 02115, USA. a.ding@neu.edu

Lifetime Data Analysis
|March 6, 2012

View abstract on PubMed

Summary
This summary is machine-generated.

We established a copula identifiability condition for dependent truncated data models. This condition, based on strong lower-left tail identifiability, is met by common Archimedean copula families.

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Area of Science:

  • Statistics
  • Probability Theory
  • Econometrics

Background:

  • Copula models are crucial for analyzing multivariate dependent data.
  • Identifiability is a fundamental property ensuring unique model parameter estimation.
  • Truncated data models present unique challenges for parameter inference.

Purpose of the Study:

  • To establish a theoretical condition for the identifiability of copulas in dependent truncated data models.
  • To characterize this identifiability using properties of the copula family's tails.
  • To verify if commonly used copula families meet the proposed condition.

Main Methods:

  • Derivation of a novel identifiability condition for copulas in truncated data.
  • Analysis of strong lower-left tail identifiability for copula families.
  • Verification of the condition for Archimedean copulas with analytic generator functions.

    • Main Results:

    • A specific condition for copula identifiability in dependent truncated data models was provided.
    • The condition was shown to be equivalent to strong lower-left tail identifiability.
    • Archimedean copula families with analytic generators were demonstrated to satisfy this identifiability condition.

    Conclusions:

    • The study provides a clear criterion for ensuring the identifiability of copula models with truncated data.
    • This finding supports the reliable application of Archimedean copulas in complex data scenarios.
    • The results contribute to the theoretical foundation of statistical modeling with dependent truncated data.