Yazarlar

• Susam, Selim Orhun(2)
• Erdoğan, Mahmut Sami(1)

• MakaleYalnız künye2022Scopus SJRQ3/r/munzur/2538
  Plug-in Estimation of Dependence Characteristics of Archimedean Copula via Bézier Curve

  Susam, Selim Orhun; Erdoğan, Mahmut Sami

  • This article introduces measurement of the dependence between variables with a dependence structure defined by Archimedean copulas. The estimation of the dependence measure, such as Kendall’s tau as well as the lower and upper tail dependence, is investigated by using estimation of the Kendall distribution function based on the Bézier curve. A Monte Carlo study is performed to measure the performance of the new estimation method. The simulation results showed that the proposed methods has good results in terms of estimation performance. The new estimators are also used to estimate the dependence coefficients for three sets of real data.

  AtıfScopus1·WoS1

  İncele
• MakaleAçık Erişim2020Scopus SJRQ4/r/munzur/3714
  Parameter estimation of some archimedean copulas based on minimum cramer-von-mises distance

  Susam, Selim Orhun

  The purpose of this paper is to introduce a new estimation method for estimating the Archimedean copula dependence parameter in the non-parametric setting. The estimation of the dependence parameter has been selected as the value that minimizes the Cramer-von-Mises distance which measures the distance between Empirical Bernstein Kendall distribution function and true Kendall distribution function. AMonte Carlo study is performedto measure the performance of thenewestimator and compared to conventional estimation methods. In terms of estimation performance, simulation results show that the proposed Minumum Cramer-von-Mises estimation method has a good performance for low dependence and a small sample size when compared with the other estimation methods. The new minimum distance estimation of the dependence parameter is applied to model the dependence of two real data sets.

  AtıfScopus13·WoS9·OpenAlex10

  İncele