A Fast Algorithm for the Coordinate-wise Minimum Distance Estimation

TL;DR

This paper introduces a fast coordinate-wise minimization algorithm for minimum distance estimation in linear regression, significantly reducing computational time and enabling practical application through an R package.

Contribution

It presents a novel, efficient algorithm for minimum distance estimation in linear regression, addressing computational challenges of traditional methods.

Findings

Algorithm significantly speeds up estimation process.

R package implementation available online.

Method maintains estimation accuracy while reducing computation time.

Abstract

Application of the minimum distance method to the linear regression model for estimating regression parameters is a difficult and time-consuming process due to the complexity of its distance function, and hence, it is computationally expensive. To deal with the computational cost, this paper proposes a fast algorithm which mainly uses technique of coordinate-wise minimization in order to estimate the regression parameters. R package based on the proposed algorithm and written in Rcpp is available online.