Afrika Statistika

Global convergence and ascent property of a cyclic algorithm used for statistical analysis of crash data

Issa Cherif Geraldo, Assi N'Gessan, and Kossi Essona Gneyou

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In this paper, we consider an estimation algorithm called cyclic iterative algorithm (CA) that is used in statistics to estimate the unknown vector parameter of a crash data model. We provide a theoretical proof of the global convergence of the CA that justifies the good numerical results obtained in early numerical studies of this algorithm. We also prove that the CA is an ascent algorithm, what ensures its numerical stability.


Dans cet article, nous considérons un algorithme d'estimation appelé algorithme cyclic iteratif (CA) utilisé en statistique pour estimer le vecteur paramètre inconnu d'une modèle pour les données d'accidents. Nous donnons une preuve th'eorique de la convergence globale du CA qui justifie les excellents résultats numériques obtenues dans les études numériques antérieures dudit algorithme. Nous prouvons aussi que le CA augmente la log-vraisemblance à chaque itération, ce qui assure sa stabilité numérique.

Article information

Afr. Stat., Volume 13, Number 2 (2018), 1631-1643.

First available in Project Euclid: 7 June 2018

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Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62-04: Explicit machine computation and programs (not the theory of computation or programming) 62F10: Point estimation 62H12: Estimation 62P99: None of the above, but in this section

Iterative method Maximum likelihood cyclic algorithm global convergence road safety


Geraldo, Issa Cherif; N'Gessan, Assi; Gneyou, Kossi Essona. Global convergence and ascent property of a cyclic algorithm used for statistical analysis of crash data. Afr. Stat. 13 (2018), no. 2, 1631--1643. doi:10.16929/as/1631.125.

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