Kernel Partial Least Square Regression with High Resistance to Multiple Outliers and Bad Leverage Points on Near-Infrared Spectral Data Analysis

Kernel Partial Least Square Regression with High Resistance to Multiple Outliers and Bad Leverage Points on Near-Infrared Spectral Data Analysis

Multivariate statistical analysis such as partial least square regression (PLSR) is the common data processing technique used to handle high-dimensional data space on near-infrared (NIR) spectral datasets. The PLSR is useful to tackle the multicollinearity and heteroscedasticity problem that can be...

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Bibliographic Details
Main Authors: Divo Dharma Silalahi, Habshah Midi, Jayanthi Arasan, Mohd Shafie Mustafa, Jean-Pierre Caliman
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Symmetry
Subjects:
partial least square regression
outliers
high leverage points
GM6 estimator
robust
nonlinear
Online Access:https://www.mdpi.com/2073-8994/13/4/547

Internet

https://www.mdpi.com/2073-8994/13/4/547

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