Abstract
Weighted cumulative residual Kullback–Leibler information measure and its dynamic version are proposed and their properties are investigated. Also weighted cumulative Kullback–Leibler information measure along with its dynamic version is introduced. Monotonicity properties of the dynamic information measures are studied. A goodness-of-fit test is developed for exponential distribution using weighted cumulative residual Kullback–Leibler information measure based on complete and censored data. It is observed that proposed tests perform well for monotone decreasing hazard alternatives under censored data. Four data sets are analyzed for illustration.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Communications in Statistics - Simulation and Computation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
