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Hazard Regression Analysis

In: Applied Nonparametric Statistics in Reliability

Author

Listed:
  • M. Luz Gámiz

    (Universidad Granada)

  • K. B. Kulasekera

    (Clemson University)

  • Nikolaos Limnios

    (Université de Technologie de Compiègne)

  • Bo Henry Lindqvist

    (Norwegian University of Science and Technology)

Abstract

This chapter is devoted to the analysis of certain models that describe in some way the relationship between the time-to-failure of a system and a set of explanatory variables (hereafter called covariates) that represent endogenous or exogenous information relevant (or maybe not) to the deterioration process of the system. The relationship between lifetime and covariates is expressed in terms of the hazard function and we consider as starting point the semi-parametric Cox proportional hazard model that is extended in several ways leading to more flexible models that may cover a wider range of practical situations. In each case, we present different techniques of estimation of the model. Finally, we focus the problem from a nonparametric point of view. Although the nonparametric estimation of the hazard function may be tackled in several ways, we consider an estimator obtained as the ratio of nonparametric estimators of the conditional density an the survival function

Suggested Citation

  • M. Luz Gámiz & K. B. Kulasekera & Nikolaos Limnios & Bo Henry Lindqvist, 2011. "Hazard Regression Analysis," Springer Series in Reliability Engineering, in: Applied Nonparametric Statistics in Reliability, chapter 0, pages 185-225, Springer.
    • Handle: RePEc:spr:ssrchp:978-0-85729-118-9_7
    DOI: 10.1007/978-0-85729-118-9_7
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