Parameter Estimates of Alpha-Stable Distribution and Hurst Coefficients. Issue 3 (29th August 2018)

• Record Type: Journal Article
• Authors: Basu, Bidroha; Pakrashi, Vikram
• Is Part Of: Journal of environmental engineering and science. Volume 13:Issue 3(2018)
• Page Start: 1
• Page End: 56
• Publication Date: 2018-08-29
• Subjects: Environmental engineering -- Periodicals; Sanitary engineering -- Periodicals; Environmental engineering -- Canada -- Periodicals; Environnement, Technique de l' -- Périodiques; Technique sanitaire -- Périodiques; Environnement, Technique de l' -- Canada -- Périodiques; Environmental engineering; Sanitary engineering; Canada; Electronic journals; Computer network resources; Periodicals; 628.05
• DOI: 10.1680/jenes.17.00027
• Languages: English
• ISSNs: 1496-2551

Abstract

Abstract : This paper presents a comparison of six different methods of estimating parameters for alpha-stable distributions on simulated and real data. Subsequently, the paper numerically investigates the relationship between estimated Hurst coefficients of the data used to fit alpha-stable distributions and the parameters of the distribution. Alpha-stable distributions are important since many real life data cannot be represented by traditional distributions and the numerical investigation relating to Hurst coefficient is motivated by the fact that many of such real life data are rich in multifractality. Real data used for this study relates to rainfall and streamflow data, which are known to have strong multifractal signature and a traditional distribution usually fails to fit such data. We show that a connection between parameter estimates of alpha-stable distributions fitted to data rich in multifractality with its Hurst coefficient may exist. Based on the simulation study it has been noted that out of the six parameter estimation approach, maximum likelihood based parameter estimation and the empirical characteristic function based parameter estimation approaches are superior in obtaining better estimate of the four alpha-stable parameters, which leads to less error in quantile estimation.