Some Inferential Results on a Two Parameter Generalized Half Normal Distribution
Issued Date
2022-01-01
Resource Type
ISSN
01966324
Scopus ID
2-s2.0-85112036488
Journal Title
American Journal of Mathematical and Management Sciences
Volume
41
Issue
3
Start Page
278
End Page
294
Rights Holder(s)
SCOPUS
Bibliographic Citation
American Journal of Mathematical and Management Sciences Vol.41 No.3 (2022) , 278-294
Suggested Citation
Sudsawat M., Pal N. Some Inferential Results on a Two Parameter Generalized Half Normal Distribution. American Journal of Mathematical and Management Sciences Vol.41 No.3 (2022) , 278-294. 294. doi:10.1080/01966324.2021.1959469 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84024
Title
Some Inferential Results on a Two Parameter Generalized Half Normal Distribution
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
A two-parameter generalized half normal distribution (2 P-GHND) is gaining attention lately due to its flexibility over other popular distributions on the positive side of the real line. Unlike gamma, lognormal or inverse Gaussian distributions, 2 P-GHND can be either negatively or positively skewed depending on its shape parameter, a property similar to Weibull distribution. In this work we address two inferential problems related to 2 P-GHND: (a) prove analytically the existence and uniqueness of the MLE of the model parameters attained through differentiation of the log-likelihood function; and (b) consider the hypothesis testing on the mean of the distribution where it is shown that a parametric bootstrap (PB) method based on the likelihood ratio test (LRT) statistic works far better than the other asymptotic tests for small to moderate sample sizes. Extensive simulation results have been provided to support this observation.