[图书][B] Introduction to robust estimation and hypothesis testing
RR Wilcox - 2012 - books.google.com
This revised book provides a thorough explanation of the foundation of robust methods,
incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and …
incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and …
Outlier detection using PCA mix based T2 control chart for continuous and categorical data
Outliers presence may lead to misdetection on out-of-control observations in Phase II,
therefore, they should be cleaned in Phase I. This paper proposes PCA Mix based T 2 chart …
therefore, they should be cleaned in Phase I. This paper proposes PCA Mix based T 2 chart …
Comparison of robust estimators for detecting outliers in multivariate datasets
SSS Abd Mutalib, SZ Satari… - Journal of Physics …, 2021 - iopscience.iop.org
Detecting outliers for multivariate data is difficult and does not work by visual inspection.
Mahalanobis distance (MD) has been a classical method to detect outliers in multivariate …
Mahalanobis distance (MD) has been a classical method to detect outliers in multivariate …
Robust joint modeling of mean and dispersion through trimming
The Maximum Likelihood Estimator (MLE) and Extended Quasi-Likelihood (EQL) estimator
have commonly been used to estimate the unknown parameters within the joint modeling of …
have commonly been used to estimate the unknown parameters within the joint modeling of …
A new robust estimator to detect outliers for multivariate data
SSS Abd Mutalib, SZ Satari… - Journal of Physics …, 2019 - iopscience.iop.org
Mahalanobis distance (MD) is a classical method to detect outliers for multivariate data.
However, classical mean and covariance matrix in MD suffered from masking and swamping …
However, classical mean and covariance matrix in MD suffered from masking and swamping …
Comparison of Robust Estimators' Performance for Detecting Outliers in Multivariate Data
SSS Abd Mutalib, SZ Satari… - Journal of Statistical …, 2021 - mjes.um.edu.my
In multivariate data, outliers are difficult to detect especially when the dimension of the data
increase. Mahalanobis distance (MD) has been one of the classical methods to detect …
increase. Mahalanobis distance (MD) has been one of the classical methods to detect …
[PDF][PDF] On robust mahalanobis distance issued from minimum vector variance
H Ali, SSS Yahaya - Far East Journal of Mathematical Sciences, 2013 - researchgate.net
Detecting outliers in high dimension datasets remains a challenging task. Under this
circumstance, robust location and scale estimators are usually proposed in place of the …
circumstance, robust location and scale estimators are usually proposed in place of the …
[PDF][PDF] The robust principal component using minimum vector variance
DE Herwindiati, SM Isa - Proceedings of the World Congress on …, 2009 - Citeseer
Principal Component Analysis (PCA) is a technique to transform the original set of variables
into a smaller set of linear combinations that account for most of the original set variance …
into a smaller set of linear combinations that account for most of the original set variance …
Outlier detection with Mahalanobis square distance: incorporating small sample correction factor
M Ekiz, OU Ekiz - Journal of Applied Statistics, 2017 - Taylor & Francis
Mahalanobis square distances (MSDs) based on robust estimators improves outlier
detection performance in multivariate data. However, the unbiasedness of robust estimators …
detection performance in multivariate data. However, the unbiasedness of robust estimators …
Impervious Surface Mapping Using Robust Depth Minimum Vector Variance Regression
This paper proposes a reliable minimum vector variance regression algorithm for robust
supervised impervious mapping. The mapping is done with a conventional two phase …
supervised impervious mapping. The mapping is done with a conventional two phase …