Noryanti, Muhammad and Coolen, Frank and Maturi, Tahani-Coolen and Norazlina, Ismail (2023) Nonparametric predictive inference with copulas for bivariate data. In: Copula modelling and its application. UTM Press, Johor, Malaysia, pp. 1-18. ISBN 978-983-52-1962-7
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Abstract
Dependencies are important in many real applications. However, identifying and modelling dependencies between two or more related random quantities is a main challenge in statistics. The dependence structure in the models will be identified before any prediction or estimation can be performed toward getting the most efficient and accurate prediction and forecasting. Analyses of dependencies are of considerable importance in many sectors as an aid to better understanding the interaction of variables in a certain field of study and as an input in every aspect of our life, including engineering, health, finance, insurance, and agriculture. Statistical dependence is a relationship between two or more characteristics of units under study or review. These units may, for example, be individuals, objects, or various aspects of the environment. The dependence structure is important in knowing whether a particular model or inference might suit a given application or data set.
| Item Type: | Book Chapter |
|---|---|
| Uncontrolled Keywords: | Nonparametric Predictive Inference; NPI with Copulas; Bivariate Data; Statistical |
| Subjects: | Q Science > QA Mathematics |
| Faculty/Division: | Center for Mathematical Science |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 06 Aug 2024 03:08 |
| Last Modified: | 06 Aug 2024 03:08 |
| URI: | http://umpir.ump.edu.my/id/eprint/42180 |
| Download Statistic: | View Download Statistics |
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