Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve is a useful tool to assess the ability of a diagnostic test to discriminate among two classes or groups. In pract...

Full description

Bibliographic Details
Main Authors:	Noryanti, Muhammad, Coolen-Maturi, Tahani, Coolen, Frank PA
Format:	Article
Language:	English
Published:	International Academic Press 2018
Subjects:	Q Science (General) QA Mathematics
Online Access:	http://umpir.ump.edu.my/id/eprint/22880/ http://umpir.ump.edu.my/id/eprint/22880/ http://umpir.ump.edu.my/id/eprint/22880/ http://umpir.ump.edu.my/id/eprint/22880/1/SOIC-FC-1708.pdf

id	ump-22880
recordtype	eprints
spelling	ump-228802019-01-16T08:21:08Z http://umpir.ump.edu.my/id/eprint/22880/ Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests Noryanti, Muhammad Coolen-Maturi, Tahani Coolen, Frank PA Q Science (General) QA Mathematics Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve is a useful tool to assess the ability of a diagnostic test to discriminate among two classes or groups. In practice, multiple diagnostic tests or biomarkers may be combined to improve diagnostic accuracy, e.g. by maximizing the area under the ROC curve. In this paper we present Nonparametric Predictive Inference (NPI) for best linear combination of two biomarkers, where the dependence of the two biomarkers is modelled using parametric copulas. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. The combination of NPI for the individual biomarkers, combined with a basic parametric copula to take dependence into account, has good robustness properties and leads to quite straightforward computation. We briefly comment on the results of a simulation study to investigate the performance of the proposed method in comparison to the empirical method. An example with data from the literature is provided to illustrate the proposed method, and related research problems are briefly discussed. International Academic Press 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22880/1/SOIC-FC-1708.pdf Noryanti, Muhammad and Coolen-Maturi, Tahani and Coolen, Frank PA (2018) Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests. Statistics, optimization and information computing., 6 (3). pp. 398-408. ISSN 2311-004X http://www.iapress.org/index.php/soic/article/view/soic.20180906/359 https://doi.org//10.19139/soic.v6i3.579
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	Q Science (General) QA Mathematics
spellingShingle	Q Science (General) QA Mathematics Noryanti, Muhammad Coolen-Maturi, Tahani Coolen, Frank PA Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests
description	Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve is a useful tool to assess the ability of a diagnostic test to discriminate among two classes or groups. In practice, multiple diagnostic tests or biomarkers may be combined to improve diagnostic accuracy, e.g. by maximizing the area under the ROC curve. In this paper we present Nonparametric Predictive Inference (NPI) for best linear combination of two biomarkers, where the dependence of the two biomarkers is modelled using parametric copulas. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. The combination of NPI for the individual biomarkers, combined with a basic parametric copula to take dependence into account, has good robustness properties and leads to quite straightforward computation. We briefly comment on the results of a simulation study to investigate the performance of the proposed method in comparison to the empirical method. An example with data from the literature is provided to illustrate the proposed method, and related research problems are briefly discussed.
format	Article
author	Noryanti, Muhammad Coolen-Maturi, Tahani Coolen, Frank PA
author_facet	Noryanti, Muhammad Coolen-Maturi, Tahani Coolen, Frank PA
author_sort	Noryanti, Muhammad
title	Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests
title_short	Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests
title_full	Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests
title_fullStr	Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests
title_full_unstemmed	Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests
title_sort	nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests
publisher	International Academic Press
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/22880/ http://umpir.ump.edu.my/id/eprint/22880/ http://umpir.ump.edu.my/id/eprint/22880/ http://umpir.ump.edu.my/id/eprint/22880/1/SOIC-FC-1708.pdf
first_indexed	2023-09-18T22:34:01Z
last_indexed	2023-09-18T22:34:01Z
_version_	1777416474601717760

Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests

Similar Items