Non-parametric conditional quantile estimation and semi-parametric learning: applications in insurance and actuarial science.

Summary The thesis is composed of two parts: one part dedicated to the estimation of conditional quantiles and another one to supervised learning. The part "Estimation of conditional quantiles" is organized in 3 chapters: Chapter 1 is devoted to an introduction on local linear regression, presenting the most used methods to estimate the smoothing parameter. Chapter 2 deals with existing methods of nonparametric estimation of the conditional quantile. These methods are compared, using numerical experiments on simulated and real data. Chapter 3 is devoted to a new estimator of the conditional quantile that we propose. This estimator is based on the use of an asymmetric kernel in x. Under certain assumptions, our estimator is more efficient than the usual estimators.
The part "Supervised learning" is also composed of 3 chapters: Chapter 4 is an introduction to statistical learning and the basic notions used in this part. Chapter 5 is a review of conventional methods of supervised classification. Chapter 6 is devoted to the transfer of a semi-parametric learning model. The performance of this method is shown by numerical experiments on morphometric data and credit-scoring data.