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#Dfind outliers in high dimension series

" Asymptotic normality of interpoint distances for high-dimensional data with applications to the two-sample problem,"īiometrika, Biometrika Trust, vol. TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer Sociedad de Estadística e Investigación Operativa, vol. " High-dimensional outlier detection using random projections," " Using visual statistical inference to better understand random class separations in high dimension, low sample size data,"Ĭomputational Statistics, Springer, vol. Niladri Roy Chowdhury & Dianne Cook & Heike Hofmann & Mahbubul Majumder & Eun-Kyung Lee & Amy Toth, 2015." The minimum weighted covariance determinant estimator for high-dimensional data,"Īdvances in Data Analysis and Classification, Springer German Classification Society - Gesellschaft für Klassifikation (GfKl) Japanese Classification Society (JCS) Classification and Data Analysis Group of the Italian Statistical Society (CLADAG) International Federation of Classification Societies (IFCS), vol. Journal of Multivariate Analysis, Elsevier, vol. " Asymptotics of hierarchical clustering for growing dimension," Borysov, Petro & Hannig, Jan & Marron, J.S., 2014.Methodology and Computing in Applied Probability, Springer, vol. " Inference on High-Dimensional Mean Vectors with Fewer Observations Than the Dimension," " Perturbation theory for cross data matrix-based PCA," Wang, Shao-Hsuan & Huang, Su-Yun, 2022." On the border of extreme and mild spiked models in the HDLSS framework," " Consistency of sparse PCA in High Dimension, Low Sample Size contexts," Shen, Dan & Shen, Haipeng & Marron, J.S., 2013." Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings,"Īnnals of the Institute of Statistical Mathematics, Springer The Institute of Statistical Mathematics, vol. Yugo Nakayama & Kazuyoshi Yata & Makoto Aoshima, 2020.

" PCA consistency for the power spiked model in high-dimensional settings,"

Yata, Kazuyoshi & Aoshima, Makoto, 2013.

" Continuum directions for supervised dimension reduction,"Ĭomputational Statistics & Data Analysis, Elsevier, vol. These are the items that most often cite the same works as this one and are cited by the same works as this one. 105(2), pages 389-402.įull references (including those not matched with items on IDEAS) " On the number of principal components in high dimensions,"īiometrika, Biometrika Trust, vol.

Sungkyu Jung & Myung Hee Lee & Jeongyoun Ahn, 2018.

" Bayesian Statistical Inference on Elliptical Matrix Distributions," " Rotation-based multiple testing in the multivariate linear model,"īiometrics, The International Biometric Society, vol. " The maximal data piling direction for discrimination,"īiometrika, Biometrika Trust, vol. " The high-dimension, low-sample-size geometric representation holds under mild conditions,"īiometrika, Biometrika Trust, vol. Journal of Productivity Analysis, Springer, vol.

" Detecting Outliers in Frontier Models: A Simple Approach," " Boundary behavior in High Dimension, Low Sample Size asymptotics of PCA,"

Jung, Sungkyu & Sen, Arusharka & Marron, J.S., 2012.

" Outlier detection for high-dimensional data,"īiometrika, Biometrika Trust, vol.

Kwangil Ro & Changliang Zou & Zhaojun Wang & Guosheng Yin, 2015.

Journal of Applied Statistics, Taylor & Francis Journals, vol. " Distance-based outlier detection for high dimension, low sample size data,"

Jeongyoun Ahn & Myung Hee Lee & Jung Ae Lee, 2019.

" Convergence of sample eigenvalues, eigenvectors, and principal component scores for ultra-high dimensional data,"īiometrika, Biometrika Trust, vol. " Outlier identification in high dimensions,"Ĭomputational Statistics & Data Analysis, Elsevier, vol.

Filzmoser, Peter & Maronna, Ricardo & Werner, Mark, 2008.

#Dfind outliers in high dimension series

Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol.

" Geometric representation of high dimension, low sample size data,"