On the Exact Asymptotic Error of the Kernel Estimator of the Conditional Hazard Function for Quasi-Associated Functional Variables

The goal of this research is to analyze the mean squared error of the kernel estimator for the conditional hazard rate, assuming that the sequence of real random vector variables (Un)n∈N satisfies the quasi-association condition. By utilizing kernel smoothing techniques and asymptotic analysis, the research derives the exact asymptotic expression for the leading terms of the quadratic error in the estimator, ensuring an accurate characterization of its convergence behavior. Additionally, an applied study using simulation is conducted to illustrate the theoretical findings. This study extends existing results on hazard rate estimation by addressing more complex dependence structures, contributing to the theory and practice of kernel-based methods in survival analysis.