Jihane Abdelli and Brahim Brahimi
In this paper, the authors applied the empirical likelihood method, which was originally proposed by Owen, to the copula moment based estimation methods to take advantage of its…
Abstract
Purpose
In this paper, the authors applied the empirical likelihood method, which was originally proposed by Owen, to the copula moment based estimation methods to take advantage of its properties, effectiveness, flexibility and reliability of the nonparametric methods, which have limiting chi-square distributions and may be used to obtain tests or confidence intervals. The authors derive an asymptotically normal estimator of the empirical likelihood based on copula moment estimation methods (ELCM). Finally numerical performance with a simulation experiment of ELCM estimator is studied and compared to the CM estimator, with a good result.
Design/methodology/approach
In this paper we applied the empirical likelihood method which originally proposed by Owen, to the copula moment based estimation methods.
Findings
We derive an asymptotically normal estimator of the empirical likelihood based on copula moment estimation methods (ELCM). Finally numerical performance with a simulation experiment of ELCM estimator is studied and compared to the CM estimator, with a good result.
Originality/value
In this paper we applied the empirical likelihood method which originally proposed by Owen 1988, to the copula moment based estimation methods given by Brahimi and Necir 2012. We derive an new estimator of copula parameters and the asymptotic normality of the empirical likelihood based on copula moment estimation methods.
Details
Keywords
Majda Kermadi, Saïd Moussaoui, Abdelhalim Taieb Brahimi and Mouloud Feliachi
This paper aims to present a data-processing methodology combining kernel change detection (KCD) and efficient global optimization algorithms for solving inverse problem in eddy…
Abstract
Purpose
This paper aims to present a data-processing methodology combining kernel change detection (KCD) and efficient global optimization algorithms for solving inverse problem in eddy current non-destructive testing. The main purpose is to reduce the computation cost of eddy current data inversion, which is essentially because of the heavy forward modelling with finite element method and the non-linearity of the parameter estimation problem.
Design/methodology/approach
The KCD algorithm is adapted and applied to detect damaged parts in an inspected conductive tube using probe impedance signal. The localization step allows in reducing the number of measurement data that will be processed for estimating the flaw characteristics using a global optimization algorithm (efficient global optimization). Actually, the minimized objective function is calculated from data related to defect detection indexes provided by KCD.
Findings
Simulation results show the efficiency of the proposed methodology in terms of defect detection and localization; a significant reduction of computing time is obtained in the step of defect characterization.
Originality/value
This study is the first of its kind that combines a change detection method (KCD) with a global optimization algorithm (efficient global optimization) for defect detection and characterization. To show that such approach allows to reduce the numerical cost of ECT data inversion.