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

Abstract

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.

Keywords:

• Bootstrap methods
• Discrete associated kernel
• Nonparametric regression
• Nonlinear regression
• Pavement design
• Survival data

Mathematics Subject Classification:

• Primary 62
• Secondary 33

Notes

*With a = 2

*With a = 2

*Age in years. **With a = 2