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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...

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Main Authors: Noryanti, Muhammad, Coolen, Frank, Maturi, Tahani-Coolen, Norazlina, Ismail
Format: Book Chapter
Language:English
English
English
Published: UTM Press 2023
Subjects:
QA Mathematics
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
http://umpir.ump.edu.my/id/eprint/42180/
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spelling my.ump.umpir.421802024-08-06T03:08:25Z 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
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
English
topic QA Mathematics
spellingShingle QA Mathematics
Noryanti, Muhammad
Coolen, Frank
Maturi, Tahani-Coolen
Norazlina, Ismail
Nonparametric predictive inference with copulas for bivariate data
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.
format Book Chapter
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
title Nonparametric predictive inference with copulas for bivariate data
title_short 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_sort nonparametric predictive inference with copulas for bivariate data
publisher UTM Press
publishDate 2023
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
http://umpir.ump.edu.my/id/eprint/42180/
_version_ 1822924569269239808
score 13.23648

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