Parameter estimation for strict arcsine distribution

The two-parameter strict arcsine distribution as a member of the natural exponential family with cubic variance function has been shown to be a viable candidate for statistical analysis of count data. Efficient methods of parameter estimation will be essential in practical applications of the distri...

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Published in:Communications in Statistics: Simulation and Computation
Main Author: Low Y.-C.; Phang Y.-N.; Khoo W.-C.; Ong S.-H.
Format: Article
Language:English
Published: Taylor and Francis Ltd. 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189816140&doi=10.1080%2f03610918.2024.2335539&partnerID=40&md5=041e72bf8b6e53dc2d51b5d8d7c21d17
id 2-s2.0-85189816140
spelling 2-s2.0-85189816140
Low Y.-C.; Phang Y.-N.; Khoo W.-C.; Ong S.-H.
Parameter estimation for strict arcsine distribution
2024
Communications in Statistics: Simulation and Computation


10.1080/03610918.2024.2335539
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189816140&doi=10.1080%2f03610918.2024.2335539&partnerID=40&md5=041e72bf8b6e53dc2d51b5d8d7c21d17
The two-parameter strict arcsine distribution as a member of the natural exponential family with cubic variance function has been shown to be a viable candidate for statistical analysis of count data. Efficient methods of parameter estimation will be essential in practical applications of the distribution. In this paper we examine some methods of parameter estimation. Due to the simple expression of the probability generating function, a probability generating function-based estimation procedure is considered and compared with other estimation procedures. Since the accuracy of the parameter estimation procedure affects the probability of correct selection in choosing the correct probability distribution, we extend the investigation by examining the discrimination between strict arcsine and generalized Poisson distributions in which both have cubic variance functions. © 2024 Taylor & Francis Group, LLC.
Taylor and Francis Ltd.
3610918
English
Article

author Low Y.-C.; Phang Y.-N.; Khoo W.-C.; Ong S.-H.
spellingShingle Low Y.-C.; Phang Y.-N.; Khoo W.-C.; Ong S.-H.
Parameter estimation for strict arcsine distribution
author_facet Low Y.-C.; Phang Y.-N.; Khoo W.-C.; Ong S.-H.
author_sort Low Y.-C.; Phang Y.-N.; Khoo W.-C.; Ong S.-H.
title Parameter estimation for strict arcsine distribution
title_short Parameter estimation for strict arcsine distribution
title_full Parameter estimation for strict arcsine distribution
title_fullStr Parameter estimation for strict arcsine distribution
title_full_unstemmed Parameter estimation for strict arcsine distribution
title_sort Parameter estimation for strict arcsine distribution
publishDate 2024
container_title Communications in Statistics: Simulation and Computation
container_volume
container_issue
doi_str_mv 10.1080/03610918.2024.2335539
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189816140&doi=10.1080%2f03610918.2024.2335539&partnerID=40&md5=041e72bf8b6e53dc2d51b5d8d7c21d17
description The two-parameter strict arcsine distribution as a member of the natural exponential family with cubic variance function has been shown to be a viable candidate for statistical analysis of count data. Efficient methods of parameter estimation will be essential in practical applications of the distribution. In this paper we examine some methods of parameter estimation. Due to the simple expression of the probability generating function, a probability generating function-based estimation procedure is considered and compared with other estimation procedures. Since the accuracy of the parameter estimation procedure affects the probability of correct selection in choosing the correct probability distribution, we extend the investigation by examining the discrimination between strict arcsine and generalized Poisson distributions in which both have cubic variance functions. © 2024 Taylor & Francis Group, LLC.
publisher Taylor and Francis Ltd.
issn 3610918
language English
format Article
accesstype
record_format scopus
collection Scopus
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