Discrete Nonparametric Kernel and Parametric Methods for the Modeling of Pavement Deterioration

Tristan Senga Kiessé (1) , Tristan Lorino (1) , Hussein Khraibani (1)

1 IFSTTAR/AME/EASE - Environnement, Aménagement, Sécurité et Eco-conception

This article is concerned with one discrete nonparametric kernel and two parametric regression approaches for providing the evolution law of pavement deterioration. The first parametric approach is a survival data analysis method; and the second is a nonlinear mixed-effects model. The nonparametric approach consists of a regression estimator using the discrete associated kernels. Some asymptotic properties of the discrete nonparametric kernel estimator are shown as, in particular, its almost sure consistency. Moreover, two data-driven bandwidth selection methods are also given, with a new theoretical explicit expression of optimal bandwidth provided for this nonparametric estimator. A comparative simulation study is realized with an application of bootstrap methods to a measure of statistical accuracy.

Mots clés: Survival data; Pavement design; Nonlinear regression; Nonparametric regression; Discrete associated kernel; Bootstrap methods

Domaines: Statistiques [math.ST]; Mathématiques [math]

Tristan Senga Kiessé, Tristan Lorino, Hussein Khraibani. Discrete Nonparametric Kernel and Parametric Methods for the Modeling of Pavement Deterioration. Communications in Statistics - Theory and Methods, 2014, 43 (6), pp.1164-1178. ⟨10.1080/03610926.2012.670355⟩. ⟨hal-01097948⟩