Binary variable extraction using nonlinear principal component analysis in classical location model
Location model is a predictive classification model that determines the groups of objects which contain mixed categorical and continuous variables. The simplest location model is known as classical location model, which can be constructed easily using maximum likelihood estimation. This model perfor...
Saved in:
Main Author: | Long, Mei Mei |
---|---|
Format: | Thesis |
Language: | eng eng |
Published: |
2016
|
Subjects: | |
Online Access: | https://etd.uum.edu.my/6007/1/s817093_01.pdf https://etd.uum.edu.my/6007/2/s817093_02.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Principal component and multiple correspondence analysis for handling mixed variables in the smoothed location model
by: Ngu, Penny Ai Huong
Published: (2016) -
One step hybrid block methods with generalised off-step points for solving directly higher order ordinary differential equations
by: Abdelrahim, Ra'ft Abdelmajid Moh'd
Published: (2016) -
Robust multiple pairwise comparison procedure for adaptive trimmed mean via P-Method
by: Low, Joon Khim
Published: (2016) -
Multinomial logistic regression probability ratio-based feature vectors for Malay vowel recognition
by: Atanda, Abdulwahab Funsho
Published: (2021) -
Robust control charts via winsorized and trimmed estimators
by: Ayu, Abdul Rahman
Published: (2020)