Eigenstructure-based angle for detecting outliers in multivariate data
There are two main reasons that motivate people to detect outliers; the first is the researchers’ intention; see the example of Mr Haldum’s cases in Barnett and Lewis. The second is the effect of outliers on analyses. This article does not differentiate between the various justifications for outlier...
Saved in:
Main Author: | Nazrina Aziz, |
---|---|
Format: | Article |
Language: | English |
Published: |
Universiti Kebangsaan Malaysia
2014
|
Online Access: | http://journalarticle.ukm.my/8160/1/21_Nazrina.pdf http://journalarticle.ukm.my/8160/ http://www.ukm.my/jsm/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Eigenstructure-based angle for detecting outliers in multivariate data
by: Aziz, Nazrina
Published: (2014) -
Eigenstructure-based control charting to detect the shift in process variability
by: Mohd. Noor, Aishah, et al.
Published: (2015) -
A review on outliers-detection methods for multivariate data
by: Sharifah Sakinah, Syed Abd Mutalib, et al.
Published: (2021) -
A new robust estimator to detect outliers for multivariate data
by: Sharifah Sakinah, Syed Abd Mutalib, et al.
Published: (2019) -
Comparison of Robust Estimators’ Performance for Detecting Outliers in Multivariate Data
by: Sharifah Sakinah, Syed Abd Mutalib, et al.
Published: (2021)