Comptes Rendus
no. 5
Statistics/Probability Theory
Statistical analysis of survival and reliability data with multiple crossings of survival functions
[Analyse statistique des données de survie et de fiabilité avec multiples effets de croisement des fonctions de survie]

Présenté par : Paul Deheuvels

Vilijandas Bagdonavičius1 ; Mikhail Nikulin2
1 University of Vilnius, 24, Naugarduko, Vilnius, Lithuania
2 Statistique mathématique, université Victor Segalen – Bordeaux 2, 146, rue Léo-Saignat, 33076 Bordeaux, France
Comptes Rendus. Mathématique, Volume 340 (2005) no. 5, pp. 377-382.

On propose un modèle et une procédure semiparamétrique d'estimation pour analyser les données de survie avec multiples effets de croisement (MCE) de fonctions de survie. Un test d'ajustement pour le modèle des risques proportionnels contre le modèle MCE est proposé.

We present a statistical model and semiparametric estimation procedure for analysis of survival data with multiple cross-effects (MCE) of survival functions. A goodness-of-fit test for the proportional hazards model against the MCE model is proposed.

Reçu le :
Accepté le :
Publié le :
DOI : 10.1016/j.crma.2005.01.019
Vilijandas Bagdonavičius 1 ; Mikhail Nikulin 2

1 University of Vilnius, 24, Naugarduko, Vilnius, Lithuania
2 Statistique mathématique, université Victor Segalen – Bordeaux 2, 146, rue Léo-Saignat, 33076 Bordeaux, France 
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     title = {Statistical analysis of survival and reliability data with multiple crossings of survival functions},
     journal = {Comptes Rendus. Math\'ematique},
     pages = {377--382},
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     number = {5},
     year = {2005},
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     language = {en},
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Vilijandas Bagdonavičius; Mikhail Nikulin. Statistical analysis of survival and reliability data with multiple crossings of survival functions. Comptes Rendus. Mathématique, Volume 340 (2005) no. 5, pp. 377-382. doi : 10.1016/j.crma.2005.01.019. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.1016/j.crma.2005.01.019/

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[2] V. Bagdonavičius; M. Nikulin Accelerated Life Models: Modeling and Statistical Analysis, Chapman and Hall/CRC, Boca Raton, 2002

[3] V. Bagdonavičius; M. Hafdi; M. Nikulin Analysis of survival data with cross-effects of survival functions, Biostatistics, Volume 5 (2004) no. 3, pp. 415-425

[4] C. Ceci, L. Mazliak, Optimal design in nonparametric life testing, Preprint January 2002, Laboratoire de probabilités et modèles aléatoirs, Universités Paris VI et VII, 2002

[5] F. Hsieh On heteroscedastic hazards regression models: theory and application, J. Roy. Statist. Soc. Ser. B, Volume 63 (2001), pp. 63-79

[6] T.R. Fleming; D.P. Harrington Counting Processes and Survival Analysis, Wiley, New York, 1991

[7] J.P. Klein; M.L. Moeschenberger Survival Analysis. Statistics for Biology and Health, Springer, New York, 1997

[8] S. Piantadosi Clinical Trials: A Methodologic Perspective, Wiley, New York, 1997

[9] H.-D.I. Wu Effect of ignoring heterogeneity in hazards regression, Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life, Birkhäuser, Boston, 2004, pp. 239-252

[10] H.-D.I. Wu, A partial score test for difference among heteroscedastic populations, Preprint of The School of Public Health, China Medical College, Taichung, Taiwan, 21 October, 2002

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