Yazarlar

• Susam, Selim Orhun(2)
• Hudaverdi, Burcu(1)

• MakaleYalnız künye2023Scopus SJRQ2/r/munzur/2854
  A weighted independence test based on smooth estimation of Kendall distribution

  Susam, Selim Orhun; Hudaverdi, Burcu

  In this study, we introduce a nonparametric test of independence which is based on a weighted Cramér-von Mises distance of the Bernstein estimate of the Kendall distribution function. We examine the power and the size of the new weighted test and, we compare it with the non-weighted nonparametric tests of independence through a Monte Carlo simulation study. The simulation results indicate that both of the choices of the weights and the polynomial degree has a significant effect on the test power. Finally, the procedure is applied to a dataset on chemical elements in water samples for the purpose of illustration.

  AtıfScopus1·WoS1·OpenAlex1

  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