Nonparametric predictive inference for combining diagnostic tests with parametric copula

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. The Receiver Operating Characteristic (ROC) curve is a popular statistical tool for describing the performance of diagnostic tests. The area under the ROC curve (AUC) is often used as...

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Main Authors:	Noryanti, Muhammad, Coolen, Frank P. A., Coolen-Maturi, Tahani
Format:	Conference or Workshop Item
Language:	English
Published:	Institute of Physics Publishing 2017
Subjects:	QA Mathematics
Online Access:	http://umpir.ump.edu.my/id/eprint/20615/ http://umpir.ump.edu.my/id/eprint/20615/ http://umpir.ump.edu.my/id/eprint/20615/1/Nonparametric%20predictive%20inference%20for%20combining%20diagnostic%20tests.pdf

id	ump-20615
recordtype	eprints
spelling	ump-206152018-07-10T02:27:11Z http://umpir.ump.edu.my/id/eprint/20615/ Nonparametric predictive inference for combining diagnostic tests with parametric copula Noryanti, Muhammad Coolen, Frank P. A. Coolen-Maturi, Tahani QA Mathematics Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. The Receiver Operating Characteristic (ROC) curve is a popular statistical tool for describing the performance of diagnostic tests. The area under the ROC curve (AUC) is often used as a measure of the overall performance of the diagnostic test. In this paper, we interest in developing strategies for combining test results in order to increase the diagnostic accuracy. We introduce nonparametric predictive inference (NPI) for combining two diagnostic test results with considering dependence structure using parametric copula. NPI is a frequentist statistical framework for inference on a future observation based on past data observations. NPI uses lower and upper probabilities to quantify uncertainty and is based on only a few modelling assumptions. While copula is a well-known statistical concept for modelling dependence of random variables. A copula is a joint distribution function whose marginals are all uniformly distributed and it can be used to model the dependence separately from the marginal distributions. In this research, we estimate the copula density using a parametric method which is maximum likelihood estimator (MLE). We investigate the performance of this proposed method via data sets from the literature and discuss results to show how our method performs for different family of copulas. Finally, we briefly outline related challenges and opportunities for future research. Institute of Physics Publishing 2017-09 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/20615/1/Nonparametric%20predictive%20inference%20for%20combining%20diagnostic%20tests.pdf Noryanti, Muhammad and Coolen, Frank P. A. and Coolen-Maturi, Tahani (2017) Nonparametric predictive inference for combining diagnostic tests with parametric copula. In: 1st International Conference on Applied & Industrial Mathematics and Statistics 2017 (ICoAIMS 2017), 8–10 August 2017 , Vistana City Centre, Kuantan, Pahang. pp. 1-6., 890 (1). ISSN 17426588 https://doi.org/10.1088/1742-6596/890/1/012129
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Noryanti, Muhammad Coolen, Frank P. A. Coolen-Maturi, Tahani Nonparametric predictive inference for combining diagnostic tests with parametric copula
description	Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. The Receiver Operating Characteristic (ROC) curve is a popular statistical tool for describing the performance of diagnostic tests. The area under the ROC curve (AUC) is often used as a measure of the overall performance of the diagnostic test. In this paper, we interest in developing strategies for combining test results in order to increase the diagnostic accuracy. We introduce nonparametric predictive inference (NPI) for combining two diagnostic test results with considering dependence structure using parametric copula. NPI is a frequentist statistical framework for inference on a future observation based on past data observations. NPI uses lower and upper probabilities to quantify uncertainty and is based on only a few modelling assumptions. While copula is a well-known statistical concept for modelling dependence of random variables. A copula is a joint distribution function whose marginals are all uniformly distributed and it can be used to model the dependence separately from the marginal distributions. In this research, we estimate the copula density using a parametric method which is maximum likelihood estimator (MLE). We investigate the performance of this proposed method via data sets from the literature and discuss results to show how our method performs for different family of copulas. Finally, we briefly outline related challenges and opportunities for future research.
format	Conference or Workshop Item
author	Noryanti, Muhammad Coolen, Frank P. A. Coolen-Maturi, Tahani
author_facet	Noryanti, Muhammad Coolen, Frank P. A. Coolen-Maturi, Tahani
author_sort	Noryanti, Muhammad
title	Nonparametric predictive inference for combining diagnostic tests with parametric copula
title_short	Nonparametric predictive inference for combining diagnostic tests with parametric copula
title_full	Nonparametric predictive inference for combining diagnostic tests with parametric copula
title_fullStr	Nonparametric predictive inference for combining diagnostic tests with parametric copula
title_full_unstemmed	Nonparametric predictive inference for combining diagnostic tests with parametric copula
title_sort	nonparametric predictive inference for combining diagnostic tests with parametric copula
publisher	Institute of Physics Publishing
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/20615/ http://umpir.ump.edu.my/id/eprint/20615/ http://umpir.ump.edu.my/id/eprint/20615/1/Nonparametric%20predictive%20inference%20for%20combining%20diagnostic%20tests.pdf
first_indexed	2023-09-18T22:29:50Z
last_indexed	2023-09-18T22:29:50Z
_version_	1777416211420676096

Nonparametric predictive inference for combining diagnostic tests with parametric copula

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