Mathematics, Volume (9), No (23), Year (2021-11) , Pages (3057-3057)

Title : ( A High-Dimensional Counterpart for the Ridge Estimator in Multicollinear Situations )

Authors: Mohammad Arashi , Mina Norouzirad , Mahdi Roozbeh , Naushad Mamode Khan ,

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Abstract

The ridge regression estimator is a commonly used procedure to deal with multicollinear data. This paper proposes an estimation procedure for high-dimensional multicollinear data that can be alternatively used. This usage gives a continuous estimate, including the ridge estimator as a particular case. We study its asymptotic performance for the growing dimension, i.e., p → ∞ when n is fixed. Under some mild regularity conditions, we prove the proposed estimator’s consistency and derive its asymptotic properties. Some Monte Carlo simulation experiments are executed in their performance, and the implementation is considered to analyze a high-dimensional genetic dataset.

Keywords

asymptotic; high–dimension; Liu estimator; multicollinear; ridge estimator
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@article{paperid:1087638,
author = {Arashi, Mohammad and Mina Norouzirad and Mahdi Roozbeh and Naushad Mamode Khan},
title = {A High-Dimensional Counterpart for the Ridge Estimator in Multicollinear Situations},
journal = {Mathematics},
year = {2021},
volume = {9},
number = {23},
month = {November},
issn = {2227-7390},
pages = {3057--3057},
numpages = {0},
keywords = {asymptotic; high–dimension; Liu estimator; multicollinear; ridge estimator},
}

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%0 Journal Article
%T A High-Dimensional Counterpart for the Ridge Estimator in Multicollinear Situations
%A Arashi, Mohammad
%A Mina Norouzirad
%A Mahdi Roozbeh
%A Naushad Mamode Khan
%J Mathematics
%@ 2227-7390
%D 2021

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