Al-Daffaie, Kadhem and Khan, Shahjahan 
ORCID: https://orcid.org/0000-0002-0446-086X
(2017)
Logistic regression for circular data.
In: 3rd ISM International Statistical Conference 2016 (ISM III): Bringing Professionalism and Prestige in Statistics, 9-11 Aug 2016, Kuala Lumpur, Malaysia.
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Text (Accepted Version)
Logistic for Circular.pdf Download (139kB) | Preview |
Abstract
This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of
Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.
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Statistics for this ePrint Item |
| Item Type: | Conference or Workshop Item (Commonwealth Reporting Category E) (Paper) |
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| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Accepted version deposited in accordance with the copyright policy of the publisher. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Date Deposited: | 31 Jan 2017 08:15 |
| Last Modified: | 19 Sep 2018 01:21 |
| Uncontrolled Keywords: | logistic regression, circular data, maximum likelihood method |
| Fields of Research (2008): | 01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics 01 Mathematical Sciences > 0104 Statistics > 010405 Statistical Theory 05 Environmental Sciences > 0502 Environmental Science and Management > 050299 Environmental Science and Management not elsewhere classified |
| Identification Number or DOI: | https://doi.org/10.1063/1.4982860 |
| URI: | http://eprints.usq.edu.au/id/eprint/30467 |
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