Development of robust procedures for partial least square regression with application to near infrared spectral data
The Partial Least Square Regression (PLSR) is a multivariate method commonly used to build a predictive model of Near Infrared (NIR) spectral data. Based on our experience, several weaknesses of the PLSR have been identified with respect to its robustness issues in the pre-processing and inproces...
Saved in:
Main Author: | Silalahi, Divo Dharma |
---|---|
Format: | Thesis |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | http://psasir.upm.edu.my/id/eprint/98710/1/IPM%202021%208%20-%20IR.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Robust estimation of a linear regression model with heteroscedastic errors /
by: Mansor, Mansor Omar
Published: (1996) -
A Robust Ridge Regression For Multicollinearity Problem In The Presence Of Outliers In The Data
by: Nur Aqilah Binti Ferdaos -
Analysis of least squares smoothing operators in the frequency domain /
by: Leong, Lap Sau
Published: (1972) -
Development of project success model using partial least squares - structural equation modeling for Malaysia SMES
by: Khan, Ahmed Ali M.
Published: (2019) -
A novel least squares based algorithm for non-stationary environments and zero tracking techniques for two-dimensional arrays /
by: Sheng, Yong
Published: (1997)