Constructing partial least squares model in the presence of missing data
Virtually all methods of data analysis are plagued by problems with missing data, and partial least squares are no exception.This work contributes to knowledge by critically examining ways of handling missing data in the estimation of Partial Least Squares Models. The experiments are performed using...
Saved in:
Main Authors: | Mohd Jamil, Jastini, Mohd Shaharanee, Izwan Nizal |
---|---|
Format: | Article |
Published: |
American Scientific Publishers
2015
|
Subjects: | |
Online Access: | http://repo.uum.edu.my/16642/ http://doi.org/10.1166/asl.2015.6120 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
RELMARK: An innovative algorithm for preprocessing of transactional business data
by: Mohd Shaharanee, Izwan Nizal, et al.
Published: (2015) -
Comparative analysis of data mining techniques for business data
by: Jamil, Jastini, et al.
Published: (2014) -
Constructing a customer satisfaction model for a utility service industry using partial least squares approach
by: Mohd Jamil, Jastini, et al.
Published: (2014) -
Estimation of vinyl acetate monomer concentration using neural network & partial least square
by: Mohamad Zafarudin, Mohamad
Published: (2008) -
Student profiling on university co-curriculum activities using data visualization tools
by: Mohd Jamil, Jastini, et al.
Published: (2017)