Goodness of fit tests for Rayleigh distribution based on Phi-divergence | Zendy

M. Mahdizadeh | Zendy; Ehsan Zamanzade | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Goodness of fit tests for Rayleigh distribution based on Phi-divergence

Author(s) -

M. Mahdizadeh,

Ehsan Zamanzade

Publication year - 2017

Publication title -

revista colombiana de estadística

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.256

H-Index - 16

eISSN - 2389-8976

pISSN - 0120-1751

DOI - 10.15446/rce.v40n2.60375

Subject(s) - goodness of fit , rayleigh distribution , divergence (linguistics) , mathematics , monte carlo method , statistics , kolmogorov–smirnov test , anderson–darling test , set (abstract data type) , statistical physics , statistical hypothesis testing , computer science , probability density function , physics , philosophy , linguistics , programming language

In this paper, we develop some goodness of fit tests for Rayleigh distribution based on Phi-divergence. Using Monte Carlo simulation, we compare the power of the proposed tests with some traditional goodness of fit tests including Kolmogorov-Smirnov, Anderson-Darling and Cramer von-Mises tests. The results indicate that the proposed tests perform well as compared with their competing tests in the literature. Finally, the proposed procedures are illustrated via a real data set.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research