Year: 2026
Type: article
Abstract: We propose a new neural network method to estimate an unknown regression function when the observed data are partially hidden due to simultaneous left- and right-censoring. The approach combines wavelet-based feature extraction with a shallow neural network, offering strong flexibility. Estimation relies on inverse probability weighting to handle censoring. We prove both standard and almost-sure convergence of the method under mild conditions on the network architecture and resolution level. The theoretical analysis draws on bias-variance decomposition, covering number arguments, and concentration inequalities.
Source: Gulf Journal of Mathematics
DOI: 10.56947/01318t10
Authors: Ryma Douas, Soumia Kharfouchi
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7162196597