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Robust Estimation for Bivariate Distribution

Contributors

Orso, Samuel

Directors

Victoria-Feser, Maria-Pia; Guerrier, Stéphane

Master program title

Master of Science in Statistics

Defense date

2013

Abstract

Copula functions are very convenient for modelling multivariate observations. Popular estimation methods are the two-stage MLE and an alternative semi-parametric with empirical cdf for the margins. Unfortunately, they are hastily biased whenever relatively small model deviations occur at the marginal (empirical or parametric) and/or copula levels. In this master thesis we propose three robust estimators that do not share this undesirable feature. The bounded-bias of robust estimators is corrected through indirect inference. By means of a simulation study we show that the robust estimators outperform the popular approaches.

Keywords

  • Bivariate distribution
  • Copula
  • Robust estimation
  • Indirect Inference
  • Income distribution

UNIGE affiliation entities

Citation (ISO format)

ORSO, Samuel. Robust Estimation for Bivariate Distribution. Master, 2013.

Main files (1)

Master thesis

Identifiers

  • PID : unige:33672
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488downloads

Technical informations

Creation

08/01/2014 10:45:00

First validation

08/01/2014 10:45:00

Update

14/03/2023 20:53:51

Status update

14/03/2023 20:53:51

Last indexation

30/10/2024 16:02:38
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