Asymptotic results of the randomly censored kernel-type expectile regression estimator for functional dependent data

Abstract This study examines the intricate process of estimating nonparametrically in expectile regression models for functional time series data that exhibit strong mixing properties within the context of a random right-censoring model. Specifically, we establish the almost complete consistency and asymptotic normality of the kernel-based expectile regression estimator. Notably, these results are derived in an asymptotic setting and are applicable under reasonably broad assumptions about the underlying model. Furthermore, we explore the practical implications of our theoretical findings in analyzing financial time series. To evaluate the performance of the proposed estimator on finite samples, we conducted comprehensive Monte Carlo simulations. These simulations provide a quantitative assessment of the estimator’s accuracy and efficiency under various scenarios, allowing for a thorough understanding of its practical utility.