Data depth approach in fitting linear regression models

被引:1
|
作者
Muthukrishnan, R. [1 ]
Kalaivani, S. [1 ]
机构
[1] Bharathiar Univ, Dept Stat, Coimbatore, Tamil Nadu, India
关键词
Linear regression; Robust regression; Regression depth; Regression depth median;
D O I
10.1016/j.matpr.2021.12.321
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The data depth approach plays a vital role in regression and multivariate analysis. It is a recently emerging research topic in statistics. Regression techniques are mainly used for analysing and modelling multifactor data, it spans a large collection of applicative scenarios in many fields such as the developing discipline of data science which includes machine learning. This paper explores the idea of regression depth. The study is carried out the computational aspects of regression depth for a given dataset under classical and robust methods, like Least Squares (LS), Least Median Squares (LMS) and S-Estimator (S) along with Regression Depth Median (RDM) approach. Further, it is demonstrated the fitted models under various methods and their efficiencies have been studied under the regression depth approach. It is observed that regression depth under robust procedures outperforms the conventional regression procedure under with and without extreme observations in the data. It is concluded that researchers can apply the data depth procedure wherever the model fitting is required when the data contains extremes. Copyright (c) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Innovation and Application in Science and Technology.
引用
收藏
页码:2212 / 2215
页数:4
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