Scandinavian Journal of Statistics· 2026Q2
Nonparametric Hazard Rate Estimation With Associated Kernels and Minimax Bandwidth Choice
- 1citations
- Q2SCImago
- 2026year
Short summary
A new theoretical framework for nonparametric hazard rate estimation using associated kernels (whose shapes depend on the estimation point) is introduced, with rigorous asymptotic results and a novel adaptive bandwidth selection method.
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Key points
- Introduces a general theoretical framework for nonparametric hazard rate estimation with associated kernels.
- Establishes asymptotic results: second-order MISE expansion and central limit theorem for the estimator.
- Proves a new oracle-type inequality for adaptive bandwidth selection, extending the Goldenshluger-Lepski method.
- Provides a systematic approach for constructing and analyzing associated kernels.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Scandinavian Journal of Statistics, 2026 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics