2011/06/15 by Brahim Brahimi, Brahimi, Brahim, Fateh Chebana +3
Computer Science · Economics, Econometrics and Finance · Environmental Science · #Computation (stat.CO) #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Hydrology and Drought Analysis #Image and Signal Denoising Methods #Methodology (stat.ME) #Primary 62G05 #Secondary 62G20
paper · pdf · doi:10.48550/arxiv.1106.2887
openalex publication_date 2011/06/15 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
Recently, Serfling and Xiao (2007) extended the L-moment theory (Hosking, 1990) to the multivariate setting. In the present paper, we focus on the two-dimension random vectors to establish a link between the bivariate L-moments (BLM) and the underlying bivariate copula functions. This connection provides a new estimate of dependence parameters of bivariate statistical data. Consistency and asymptotic normality of the proposed estimator are established. Extensive simulation study is carried out to compare estimators based on the BLM, the maximum likelihood, the minimum distance and rank approximate Z-estimation. The obtained results show that, when the sample size increases, BLM-based estimation performs better as far as the bias and computation time are concerned. Moreover, the root mean squared error (RMSE) is quite reasonable and less sensitive in general to outliers than those of the above cited methods. Further, we expect that the BLM method is an easy-to-use tool for the estimation of multiparameter copula models.