Median and Extreme Ranked Set Sampling for penalized spline estimation

Authors

Ali Algarni, Statistics Department, Faculty of Science, King Abdulaziz University, Jeddah, Saudi ArabiaFollow

Author Country (or Countries)

Saudi Arabia

Abstract

This paper improves and demonstrates two approaches of Ranked Set Sampling (RSS) method for penalized spline models which are Median and Extreme RSS. These improved methods increase the efficiency of the estimated parameters in the targeted model with comparing to usual RSS and Simple Random Sampling (SRS). Moreover, in practical studies, our improved methods can reduce sampling expenses dramatically. The paper approaches are illustrated using a simulation study as well as a practical example.

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/amis/100124

Recommended Citation

Algarni, Ali (2016) "Median and Extreme Ranked Set Sampling for penalized spline estimation," Applied Mathematics & Information Sciences: Vol. 10: Iss. 1, Article 24.
DOI: http://dx.doi.org/10.18576/amis/100124
Available at: https://digitalcommons.aaru.edu.jo/amis/vol10/iss1/24