Outlier detection for multinomial data with a large number of categories

This paper develops an outlier detection procedure for multinomial data when the number of categories tends to infinity. Most of the outlier detection methods are based on the assumption that the o...

[1]  Carl N. Morris,et al.  CENTRAL LIMIT THEOREMS FOR MULTINOMIAL SUMS , 1975 .

[2]  J. Romo,et al.  On the Concept of Depth for Functional Data , 2009 .

[3]  Kang-Mo Jung Multivariate least-trimmed squares regression estimator , 2005, Comput. Stat. Data Anal..

[4]  P. Rousseeuw Least Median of Squares Regression , 1984 .

[5]  José Julio Espina Agulló,et al.  The multivariate least-trimmed squares estimator , 2008 .

[6]  Christophe Croux,et al.  Sparse least trimmed squares regression for analyzing high-dimensional large data sets , 2013, 1304.4773.

[7]  A power divergence test in the problem of sample homogeneity for large numbers of outcomes and trials , 2005 .

[8]  Benjamin Thyreau,et al.  Detecting Outlying Subjects in High-Dimensional Neuroimaging Datasets with Regularized Minimum Covariance Determinant , 2011, MICCAI.

[9]  Kwang-Ho Ro,et al.  Outlier detection for high-dimensional data , 2015 .

[10]  Peter Filzmoser,et al.  Outlier identification in high dimensions , 2008, Comput. Stat. Data Anal..

[11]  Stefan Van Aelst,et al.  MULTIVARIATE REGRESSION S-ESTIMATORS FOR ROBUST ESTIMATION AND INFERENCE , 2005 .

[12]  Nan Chen,et al.  Multivariate Exponentially Weighted Moving-Average Chart for Monitoring Poisson Observations , 2015 .

[13]  Michael Pokojovy,et al.  A Multistep, Cluster-Based Multivariate Chart for Retrospective Monitoring of Individuals , 2009 .

[14]  Nan Chen,et al.  Projection-based outlier detection in functional data , 2017 .

[15]  Michael Pokojovy,et al.  A Cluster-Based Outlier Detection Scheme for Multivariate Data , 2015 .

[16]  Guanghui Wang,et al.  Change-point detection in multinomial data with a large number of categories , 2018, The Annals of Statistics.

[17]  Guan Yu,et al.  Outlier Detection in Functional Observations With Applications to Profile Monitoring , 2012, Technometrics.

[18]  M. Febrero,et al.  Outlier detection in functional data by depth measures, with application to identify abnormal NOx levels , 2008 .