Pages that link to "Item:Q432317"

The following pages link to Boundary behavior in high dimension, low sample size asymptotics of PCA (Q432317):

Displaying 23 items.

• Significance analysis of high-dimensional, low-sample size partially labeled data (Q286481) (← links)
• PCA consistency for the power spiked model in high-dimensional settings (Q391897) (← links)
• PCA and eigen-inference for a spiked covariance model with largest eigenvalues of same asymptotic order (Q398204) (← links)
• On the border of extreme and mild spiked models in the HDLSS framework (Q413760) (← links)
• Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations (Q764487) (← links)
• Asymptotic properties of the first principal component and equality tests of covariance matrices in high-dimension, low-sample-size context (Q899373) (← links)
• Bounds on the quality of the PCA bounding boxes (Q1028234) (← links)
• PCA consistency in high dimension, low sample size context (Q1043724) (← links)
• Continuum directions for supervised dimension reduction (Q1662921) (← links)
• Subspace rotations for high-dimensional outlier detection (Q2022542) (← links)
• A note on combined inference on the common coefficient of variation using confidence distributions (Q2259534) (← links)
• Using visual statistical inference to better understand random class separations in high dimension, low sample size data (Q2354730) (← links)
• The statistics and mathematics of high dimension low sample size asymptotics (Q2828626) (← links)
• Asymptotic Distribution of Studentized Contribution Ratio in High-Dimensional Principal Component Analysis (Q3625358) (← links)
• PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context (Q3644996) (← links)
• A High-Dimensional Two-Sample Test for Non-Gaussian Data under a Strongly Spiked Eigenvalue Model (Q4578226) (← links)
• A survey of high dimension low sample size asymptotics (Q4639812) (← links)
• (Q5011468) (← links)
• Adjusting systematic bias in high dimensional principal component scores (Q5066782) (← links)
• A classifier under the strongly spiked eigenvalue model in high-dimension, low-sample-size context (Q5077372) (← links)
• Binary discrimination methods for high-dimensional data with a geometric representation (Q5160208) (← links)
• The high-dimension, low-sample-size geometric representation holds under mild conditions (Q5447664) (← links)
• Double data piling: a high-dimensional solution for asymptotically perfect multi-category classification (Q6643296) (← links)