Nonparametric predictive inference with copulas for bivariate data
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...
| Main Authors: | , , , |
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| Format: | Book Chapter |
| Language: | English English English |
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UTM Press
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42180/1/Cover%20-%20Copula%20modelling%20and%20its%20application.pdf http://umpir.ump.edu.my/id/eprint/42180/2/Intro%20-%20Nonparametric%20predictive%20inference%20with%20copulas%20for%20bivariate%20data.pdf http://umpir.ump.edu.my/id/eprint/42180/3/Nonparametric%20predictive%20inference%20with%20copulas%20for%20bivariate%20data.pdf |
| _version_ | 1811138562695888896 |
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| author | Noryanti, Muhammad Coolen, Frank Maturi, Tahani-Coolen Norazlina, Ismail |
| author_facet | Noryanti, Muhammad Coolen, Frank Maturi, Tahani-Coolen Norazlina, Ismail |
| author_sort | Noryanti, Muhammad |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-09-25T03:52:10Z |
| format | Book Chapter |
| id | UMPir42180 |
| institution | Universiti Malaysia Pahang |
| language | English English English |
| last_indexed | 2024-09-25T03:52:10Z |
| publishDate | 2023 |
| publisher | UTM Press |
| record_format | dspace |
| spelling | UMPir421802024-08-06T03:08:25Z http://umpir.ump.edu.my/id/eprint/42180/ Nonparametric predictive inference with copulas for bivariate data Noryanti, Muhammad Coolen, Frank Maturi, Tahani-Coolen Norazlina, Ismail QA Mathematics 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. UTM Press 2023 Book Chapter PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42180/1/Cover%20-%20Copula%20modelling%20and%20its%20application.pdf pdf en http://umpir.ump.edu.my/id/eprint/42180/2/Intro%20-%20Nonparametric%20predictive%20inference%20with%20copulas%20for%20bivariate%20data.pdf pdf en http://umpir.ump.edu.my/id/eprint/42180/3/Nonparametric%20predictive%20inference%20with%20copulas%20for%20bivariate%20data.pdf 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 |
| spellingShingle | QA Mathematics Noryanti, Muhammad Coolen, Frank Maturi, Tahani-Coolen Norazlina, Ismail Nonparametric predictive inference with copulas for bivariate data |
| title | Nonparametric predictive inference with copulas for bivariate data |
| title_full | Nonparametric predictive inference with copulas for bivariate data |
| title_fullStr | Nonparametric predictive inference with copulas for bivariate data |
| title_full_unstemmed | Nonparametric predictive inference with copulas for bivariate data |
| title_short | Nonparametric predictive inference with copulas for bivariate data |
| title_sort | nonparametric predictive inference with copulas for bivariate data |
| topic | QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/42180/1/Cover%20-%20Copula%20modelling%20and%20its%20application.pdf http://umpir.ump.edu.my/id/eprint/42180/2/Intro%20-%20Nonparametric%20predictive%20inference%20with%20copulas%20for%20bivariate%20data.pdf http://umpir.ump.edu.my/id/eprint/42180/3/Nonparametric%20predictive%20inference%20with%20copulas%20for%20bivariate%20data.pdf |
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