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Modifed almost unbiased two-parameter estimator for the Poisson regression model with an application to accident data
Univ Anbar, Dept Math, Coll Educ Pure Sci, Ramadi, Iraq..
Jönköping University, Jönköping International Business School, JIBS, Statistics.ORCID iD: 0000-0003-0279-5305
Jönköping University, Jönköping International Business School, JIBS, Statistics.ORCID iD: 0000-0002-4535-3630
Florida Int Univ, Dept Math & Stat, Miami, FL 33199 USA..
2021 (English)In: SORT - Statistics and Operations Research Transactions, ISSN 1696-2281, E-ISSN 2013-8830, Vol. 45, no 2, p. 121-142Article in journal (Refereed) Published
Abstract [en]

Due to the large amount of accidents negatively affecting the wellbeing of the survivors and their families, a substantial amount of research is conducted to determine the causes of road accidents. This type of data come in the form of non-negative integers and may be modelled using the Poisson regression model. Unfortunately, the commonly used maximum likelihood estimator is unstable when the explanatory variables of the Poisson regression model are highly correlated. Therefore, this paper proposes a new almost unbiased estimator which reduces the instability of the maximum likelihood estimator and at the same time produce smaller mean squared error. We study the sta-tistical properties of the proposed estimator and a simulation study has been conducted to compare the performance of the estimators in the smaller mean squared error sense. Finally, Swedish traffic fatality data are analyzed to show the beneft of the proposed method.

Place, publisher, year, edition, pages
INST ESTADISTICA CATALUNYA-IDESCAT , 2021. Vol. 45, no 2, p. 121-142
Keywords [en]
Applied traffic modeling, Maximum likelihood estimator, Mean squared error matrix, Poisson regression, Simulation study, Traffic fatality
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hj:diva-55503DOI: 10.2436/20.8080.02.112ISI: 000734066700002Scopus ID: 2-s2.0-85122852453Local ID: POA;intsam;789357OAI: oai:DiVA.org:hj-55503DiVA, id: diva2:1626399
Available from: 2022-01-11 Created: 2022-01-11 Last updated: 2022-01-25Bibliographically approved

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Qasim, MuhammadMånsson, Kristofer

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