A Critical Study on Assessing M-Estimation and It’s Approaches for Solving Some Linear Programming and Non-Linear Regression Problems

Exploring the Properties and Applications of M-Estimation in Linear Programming and Regression Analysis

Abstract

M-estimation method for the high-dimensional linear regression model and discussion about the properties of the M-estimator when the discipline term is a neighborhood linear supposition. Believe it or not, the M-estimation method is a structure which covers the methods of the least through and through deviation, the quantile regression, the least squares regression and the Huber regression. We show that the proposed estimator has the extraordinary properties by applying certain doubts. In the bit of the numerical multiplication, we select the appropriate estimation to show the incredible heartiness of this method. The least-squares estimation system which limits the whole of the squared residuals is exceedingly unstable to special cases. One standard fix is to restrict distinctive components of the residuals that down weight extensive residuals. Another way to deal with this method is proposed. Additional inquiries that grant to recognize and model the special cases are exhibited. In this Paper the examinations are obtained using a standard quadratic programming schedule. Developments to the full scale least-squares model inside seeing exemptions are proposed