Influence diagnostics in Log-Logistic regression model with censored data

Log-Logistic regression model arise in several areas of application. Traditional estimation methods for Log-Logistic regression model are sensitive to influential observations. Such bizarre observations can isolate analysis and lead to incorrect conclusions and actions. We suggest local influence di...

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Autores principales:	Javeria Khaleeq, Muhammad Amanullah, Alanazi Talal Abdulrahman, E.H. Hafez, M.M.Abd El-Raouf
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Log-Logistic distribution Censoring Generalized Cook’s Distance Local influence Perturbation Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/1e2e21f74f1f437c98f9f937b2f5aeb6
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spelling	oai:doaj.org-article:1e2e21f74f1f437c98f9f937b2f5aeb62021-12-02T04:59:37ZInfluence diagnostics in Log-Logistic regression model with censored data1110-016810.1016/j.aej.2021.06.097https://doaj.org/article/1e2e21f74f1f437c98f9f937b2f5aeb62022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1110016821004580https://doaj.org/toc/1110-0168Log-Logistic regression model arise in several areas of application. Traditional estimation methods for Log-Logistic regression model are sensitive to influential observations. Such bizarre observations can isolate analysis and lead to incorrect conclusions and actions. We suggest local influence diagnostics for identifying unusual observations in Log-Logistic regression model with censored data. The diagnostic methods under the perturbation scheme of case weight, explanatory and response variables are derived. Computational statistical measures are proposed that make the procedures practicable. Moreover, Generalized Cook’s distance and One-step Newton-Raphson method are also studied. Finally, a real data set and simulation study is presented. The results of illustrative example and simulation scheme clearly reveal that the proposed diagnostic methods under normal curvature perform better than others.Javeria KhaleeqMuhammad AmanullahAlanazi Talal AbdulrahmanE.H. HafezM.M.Abd El-RaoufElsevierarticleLog-Logistic distributionCensoringGeneralized Cook’s DistanceLocal influencePerturbationEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 3, Pp 2230-2241 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Log-Logistic distribution Censoring Generalized Cook’s Distance Local influence Perturbation Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Log-Logistic distribution Censoring Generalized Cook’s Distance Local influence Perturbation Engineering (General). Civil engineering (General) TA1-2040 Javeria Khaleeq Muhammad Amanullah Alanazi Talal Abdulrahman E.H. Hafez M.M.Abd El-Raouf Influence diagnostics in Log-Logistic regression model with censored data
description	Log-Logistic regression model arise in several areas of application. Traditional estimation methods for Log-Logistic regression model are sensitive to influential observations. Such bizarre observations can isolate analysis and lead to incorrect conclusions and actions. We suggest local influence diagnostics for identifying unusual observations in Log-Logistic regression model with censored data. The diagnostic methods under the perturbation scheme of case weight, explanatory and response variables are derived. Computational statistical measures are proposed that make the procedures practicable. Moreover, Generalized Cook’s distance and One-step Newton-Raphson method are also studied. Finally, a real data set and simulation study is presented. The results of illustrative example and simulation scheme clearly reveal that the proposed diagnostic methods under normal curvature perform better than others.
format	article
author	Javeria Khaleeq Muhammad Amanullah Alanazi Talal Abdulrahman E.H. Hafez M.M.Abd El-Raouf
author_facet	Javeria Khaleeq Muhammad Amanullah Alanazi Talal Abdulrahman E.H. Hafez M.M.Abd El-Raouf
author_sort	Javeria Khaleeq
title	Influence diagnostics in Log-Logistic regression model with censored data
title_short	Influence diagnostics in Log-Logistic regression model with censored data
title_full	Influence diagnostics in Log-Logistic regression model with censored data
title_fullStr	Influence diagnostics in Log-Logistic regression model with censored data
title_full_unstemmed	Influence diagnostics in Log-Logistic regression model with censored data
title_sort	influence diagnostics in log-logistic regression model with censored data
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/1e2e21f74f1f437c98f9f937b2f5aeb6
work_keys_str_mv	AT javeriakhaleeq influencediagnosticsinloglogisticregressionmodelwithcensoreddata AT muhammadamanullah influencediagnosticsinloglogisticregressionmodelwithcensoreddata AT alanazitalalabdulrahman influencediagnosticsinloglogisticregressionmodelwithcensoreddata AT ehhafez influencediagnosticsinloglogisticregressionmodelwithcensoreddata AT mmabdelraouf influencediagnosticsinloglogisticregressionmodelwithcensoreddata
_version_	1718400877274857472

Influence diagnostics in Log-Logistic regression model with censored data

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