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...
Published in: | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION |
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Main Authors: | , , , , |
Format: | Article; Early Access |
Language: | English |
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TAYLOR & FRANCIS INC
2024
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Subjects: | |
Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001195705000001 |
author |
Low Yeh-Ching; Phang Yook-Ngor; Khoo Wooi-Chen; Ong Seng-Huat |
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spellingShingle |
Low Yeh-Ching; Phang Yook-Ngor; Khoo Wooi-Chen; Ong Seng-Huat Parameter estimation for strict arcsine distribution Mathematics |
author_facet |
Low Yeh-Ching; Phang Yook-Ngor; Khoo Wooi-Chen; Ong Seng-Huat |
author_sort |
Low |
spelling |
Low, Yeh-Ching; Phang, Yook-Ngor; Khoo, Wooi-Chen; Ong, Seng-Huat Parameter estimation for strict arcsine distribution COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION English Article; Early Access 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. TAYLOR & FRANCIS INC 0361-0918 1532-4141 2024 10.1080/03610918.2024.2335539 Mathematics WOS:001195705000001 https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001195705000001 |
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 |
container_title |
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION |
language |
English |
format |
Article; Early Access |
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. |
publisher |
TAYLOR & FRANCIS INC |
issn |
0361-0918 1532-4141 |
publishDate |
2024 |
container_volume |
|
container_issue |
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doi_str_mv |
10.1080/03610918.2024.2335539 |
topic |
Mathematics |
topic_facet |
Mathematics |
accesstype |
|
id |
WOS:001195705000001 |
url |
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001195705000001 |
record_format |
wos |
collection |
Web of Science (WoS) |
_version_ |
1809678907891253248 |