A new wavelet-based estimation of conditional density via block threshold method

Abstract

A new wavelet-based estimator of the conditional density is investigated. The estimator is constructed by combining a special ratio technique and applying a non negative estimator to the density function in the denominator. We used a wavelet shrinkage technique to find an adaptive estimator for this problem. In particular, a block thresholding estimator is proposed, and we prove that it enjoys powerful mean integrated squared error properties over Besov balls. Moreover, it is shown that convergence rates for the mean integrated squared error (MISE) of the adaptive estimator are optimal under some mild assumptions. Finally, a numerical example has been considered to illustrate the performance of the estimator.

Reference

Olivier Faugeras, and Esmaeil Shirazi, “A new wavelet-based estimation of conditional density via block threshold method”, Communications in Statistics - Theory and Methods, 2024, forthcoming.