Using Cuckoo Algorithm for Estimating Two GLSD Parameters and Comparing it with Other Algorithms
J. Stephan, H. Hasan, and A. Omran. International Journal of Computer Science and Information Technology (IJCSIT), 9 (5):
87 - 96(October 2017)
Abstract
This study introduces and compares different methods for estimating the two parameters of generalized
logarithmic series distribution. These methods are the cuckoo search optimization, maximum likelihood
estimation, and method of moments algorithms. All the required derivations and basic steps of each
algorithm are explained. The applications for these algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50, 100). Results are compared using the statistical measure mean square error.
%0 Journal Article
%1 stephanusing
%A Stephan, Jane Jaleel
%A Hasan, Haitham Sabah
%A Omran, Alaa Hamza
%D 2017
%J International Journal of Computer Science and Information Technology (IJCSIT)
%K (CSO) (MLE) (MOM) (MSE) Cuckoo algorithm error estimation likelihood maximum mean method moments of optimization search square
%N 5
%P 87 - 96
%T Using Cuckoo Algorithm for Estimating Two GLSD Parameters and Comparing it with Other Algorithms
%U http://airccse.org/journal/ijcsit2017_curr.html
%V 9
%X This study introduces and compares different methods for estimating the two parameters of generalized
logarithmic series distribution. These methods are the cuckoo search optimization, maximum likelihood
estimation, and method of moments algorithms. All the required derivations and basic steps of each
algorithm are explained. The applications for these algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50, 100). Results are compared using the statistical measure mean square error.
@article{stephanusing,
abstract = {This study introduces and compares different methods for estimating the two parameters of generalized
logarithmic series distribution. These methods are the cuckoo search optimization, maximum likelihood
estimation, and method of moments algorithms. All the required derivations and basic steps of each
algorithm are explained. The applications for these algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50, 100). Results are compared using the statistical measure mean square error.},
added-at = {2021-04-28T06:56:16.000+0200},
author = {Stephan, Jane Jaleel and Hasan, Haitham Sabah and Omran, Alaa Hamza},
biburl = {https://www.bibsonomy.org/bibtex/2879617988c62fc8d3d7f57416a29d6af/shamerjose},
interhash = {aea867bf53c3a6b23dd06225f4517488},
intrahash = {879617988c62fc8d3d7f57416a29d6af},
journal = { International Journal of Computer Science and Information Technology (IJCSIT)},
keywords = {(CSO) (MLE) (MOM) (MSE) Cuckoo algorithm error estimation likelihood maximum mean method moments of optimization search square},
month = oct,
number = 5,
pages = {87 - 96},
timestamp = {2021-04-28T06:56:16.000+0200},
title = {Using Cuckoo Algorithm for Estimating Two GLSD Parameters and Comparing it with Other Algorithms},
url = {http://airccse.org/journal/ijcsit2017_curr.html},
volume = 9,
year = 2017
}