Work

Modal Regression Estimation by Local Linear Approach in High-Dimensional Data Case

Year: 2025

Type: article

Abstract: This paper introduces a new nonparametric estimator for detecting the conditional mode in the functional input variable setting. The estimator integrates a local linear approach with an L1-robust algorithm and treats the modal regression as the minimizer of the quantile derivative. As an asymptotic result, we derive the theoretical properties of the estimator by analyzing its convergence rate under the almost complete consistency framework. The result is stated under standard conditions, characterizing both the functional structure of the data and the local linear approximation properties of the model. Moreover, the expression of the convergence rate retains the usual form of the stochastic convergence rate in functional statistics. Simulations and real-data applications demonstrate the algorithm’s effectiveness, showing its advantage over existing methods in high-dimensional prediction tasks.

Source: Axioms

DOI: 10.3390/axioms14070537

Authors: Fatimah A. Almulhim, Mohammed B. Alamari, Ali Laksaci, Zoulikha Kaid

Institutions: Princess Nourah bint Abdulrahman University, King Khalid University

Language: English

Topic: Advanced Statistical Methods and Models

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Princess Nourah Bint Abdulrahman University, King Khalid University