Document Type : Original Paper
Authors
Department of Statistics, Faculty of Sciences, University of Abuja, Abuja, Nigeria
Abstract
The proposed research incorporates the utilization of a heavy-tailed skewed distribution referred to as the inverse Weibull as a link function in the context of a binary classification model. This selection is motivated by the need to address the existence of rare or extreme events in random processes. The study introduces a model that relies on the Inverse Weibull (TYPE II) distribution, and the estimation of model parameters is accomplished through the application of maximum likelihood methods. When the outcomes are compared to those derived from other link functions such as TYPE I (Complementary log) and TYPE III (Weibull) based on extreme value distributions using standard classification data as well as real-life data, it becomes apparent that the Inverse Weibull (TYPE II) model exhibits exceptional performance. This assessment of performance takes into account several criteria, encompassing the Akaike information criterion, Bayesian information criterion, Area under the curve, and Brier scores. In conclusion, the study establishes that the proposed model demonstrates considerable robustness in its performance, rendering it a viable choice for the modeling of binary classification problems.
Keywords
Main Subjects
- Harrison J. Bayesian Forecasting & Dynamic Models. 1999. Available from: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Bayesian+Forecasting+and+Dynamic+Models#1
- Li T-H, Macdonald IL, Zucchini W. Hidden Markov and Other Models for Discrete- Valued Time Series. J Am Stat Assoc. 1998;93(444):1525.
- Caron R, Polpo A. Binary data regression: Weibull distribution. AIP Conf Proc. 2009;1193:187–193.
- Caron R, Sinha D, Dey DK, Polpo A. Categorical Data Analysis Using a Skewed Weibull Regression Model. 2018;1–17. https://doi.org/10.3390/e20030176.
- Almeida M, Blondell L, Peralta JM Jr, K JW Jr, Jun G, Teslovich TM, et al. Independent test assessment using the extreme value distribution theory. BMC Proceed. 2016;10(Suppl 7):5–9.
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- Rinne H. The Weibull Distribution. Taylor & Francis Group, LLC. 2009.
- Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugenics. 1936;7(2):179-188.
- Taylor P, Chen M, Dey DK, Shao O. A New Skewed Link Model for Dichotomous Quantal Response Data. J Am Stat Assoc. 2013;37–41.
- Tahir MH, Cordeiro GM, Alzaatreh A, Mansoor M, Zubair M. The logistic-X family of distributions and its applications. Commun Stat Hteory Methods. 2016;45(24):7326-49.
- Berhe T, Modibia LM, Sahile AT, Tedla GW. Does quality of antenatal care influence antepartum stillbirth in Hossana City, South Ethiopia? PLOS Glob Public Health. 2023;3(1):e0001468.
- Orisakwe IC, Nwofor OK, Njoku CC, Ezedigboh UO. On the analysis of the changes in the temperatures over Abuja, Nigeria. J Physic Sci Environ Stud. 2017;3(1):8–17.
- Afulani PA. Determinants of stillbirths in Ghana: Does quality of antenatal care matter? BMC Pregnancy Childbirth. 2016;16(1):1–17.
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