Kernel Estimator of the Conditional Hazard Function for Truncated Dependent Data with Functional Regressors
Abstract
In this paper, we propose a new non-parametric kernel estimator of the conditional hazard function where the explanatory variable takes values in an infinite-dimensional space and the response variable is subject to random left truncation and satisfies the alpha-mixing property. Our main aim is to prove the strong uniform consistency rate for both functional and real arguments of the estimator. Furthermore, the performance of our estimator is evaluated via a simulation study and its practical relevance is illustrated by an application to real-world data.
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