Selecting the number of principal components: estimation of the true rank of a noisy matrix

Yun‐Jin Choi, Jonathan Taylor, Robert Tibshirani

Type: Preprint

Publication Date: 2014-01-01

Citations: 1

DOI: https://doi.org/10.48550/arxiv.1410.8260

View Publication

Locations

DataCite API - View
arXiv (Cornell University) - View - PDF
Project Euclid (Cornell University) - View - PDF

Similar Works

Action	Title	Year	Authors
+	Selecting the number of principal components: estimation of the true rank of a noisy matrix	2014	Yunjin Choi Jonathan Taylor Robert Tibshirani
+	Selecting the number of principal components: Estimation of the true rank of a noisy matrix	2017	Yunjin Choi Jonathan E. Taylor Robert Tibshirani
+	On estimation of the noise variance in high-dimensional probabilistic principal component analysis	2013	Damien Passemier Zhaoyuan Li Jianfeng Yao
+ PDF Chat	Estimating the number of signals using principal component analysis	2019	Joni Virta Klaus Nordhausen
+	Selecting the number of components in PCA via random signflips	2020	David Hong Yue Sheng Edgar Dobriban
+	When are the most informative components for inference also the principal components	2013	Raj Rao Nadakuditi
+	When are the most informative components for inference also the principal components?	2013	Raj Rao Nadakuditi
+	A randomized algorithm for principal component analysis	2008	Vladimir Rokhlin Arthur Szlam Mark Tygert
+	A randomized algorithm for principal component analysis	2008	Vladimir Rokhlin Arthur Szlam Mark Tygert
+ PDF Chat	A Randomized Algorithm for Principal Component Analysis	2009	Vladimir Rokhlin Arthur Szlam Mark Tygert
+ PDF Chat	Application of Random Matrix Theory in High-Dimensional Statistics	2024	Swapnaneel Bhattacharyya Srijan Chattopadhyay Sevantee Basu
+	Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated	2016	Namrata Vaswani Han Guo
+	Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated	2016	Namrata Vaswani Han Guo
+	Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated	2016	Namrata Vaswani Han Guo
+	Determining the number of correlated signals between two data sets using PCA-CCA when sample support is extremely small	2015	Yang Song Peter J. Schreier Nicholas J. Roseveare
+	Feasible model-based principal component analysis: Joint estimation of rank and error covariance matrix	2024	Tak-Shing Chan Alex Gibberd
+	Low-Rank Principal Eigenmatrix Analysis	2019	Krishna Balasubramanian Elynn Y. Chen Jianqing Fan Xiang Wu
+	A generalized information criterion for high-dimensional PCA rank selection	2020	Hung Hung Suyun Huang Ching‐Kang Ing
+ PDF Chat	Principal component analysis for max-stable distributions	2024	Felix Reinbott Anja Janßen
+	De-Biased Sparse PCA: Inference for Eigenstructure of Large Covariance Matrices	2021	Jana Janková Sara van de Geer

Works That Cite This (0)

Action	Title	Year	Authors

Works Cited by This (0)

Action	Title	Year	Authors