Work

Nonparametric Hazard Rate Estimation With Associated Kernels and Minimax Bandwidth Choice

Year: 2026

Type: article

Abstract: ABSTRACT In this paper, we introduce a general theoretical framework for nonparametric hazard rate estimation using associated kernels, whose shapes depend on the point of estimation. Within this framework, we establish rigorous asymptotic results, including a second‐order expansion of the MISE, and a central limit theorem for the proposed estimator. We also prove a new oracle‐type inequality for both local and global adaptive bandwidth selection, extending the Goldenshluger‐Lepski method to the context of associated kernels. Our results propose a systematic way to construct and analyze new associated kernels. Finally, we show that the general framework applies to the Gamma kernel, and we provide several examples of applications on simulated data and experimental data for the study of aging.

Source: Scandinavian Journal of Statistics

DOI: 10.1111/sjos.70096

Authors: Luce Breuil, Sarah Kaakaï