Principal component and multiple correspondence analysis for handling mixed variables in the smoothed location model
The issue of classifying objects into groups when the measured variables are mixtures of continuous and binary variables has attracted the attention of statisticians. Among the discriminant methods in classification, Smoothed Location Model (SLM) is used to handle data that contains both continuous...
Saved in:
Main Author: | Ngu, Penny Ai Huong |
---|---|
Format: | Thesis |
Language: | eng eng |
Published: |
2016
|
Subjects: | |
Online Access: | https://etd.uum.edu.my/6034/1/s817094_01.pdf https://etd.uum.edu.my/6034/2/s817094_02.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhanced single exponential smoothing technique for extreme data
by: Noor Shahifah, Muhamad
Published: (2015) -
Binary variable extraction using nonlinear principal component analysis in classical location model
by: Long, Mei Mei
Published: (2016) -
Group Method of Data Handling model using modified Levenberg-Marquardt technique
by: Roudbaneh, Maryam Pournasir
Published: (2013) -
Interacted Multiple Ant Colonies for Search Stagnation Problem
by: Aljanabi, Alaa Ismael
Published: (2010) -
Monte Carlo Simulations Of Avalanche Multiplication, Noise And Speed In Submicron InP
by: You , Ah Heng
Published: (2005)