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Robust Estimation for Bivariate Distribution

ContributorsOrso, Samuel
Master program titleMaster of Science in Statistics
Defense date2013
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.

eng
Keywords
  • Bivariate distribution
  • Copula
  • Robust estimation
  • Indirect Inference
  • Income distribution
Citation (ISO format)
ORSO, Samuel. Robust Estimation for Bivariate Distribution. 2013.
Main files (1)
Master thesis
accessLevelPublic
Identifiers
  • PID : unige:33672
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Technical informations

Creation01/08/2014 10:45:00 AM
First validation01/08/2014 10:45:00 AM
Update time03/14/2023 8:53:51 PM
Status update03/14/2023 8:53:51 PM
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