Pages that link to "Item:Q990890"

The following pages link to Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix (Q990890):

Displaying 34 items.

• High-dimensional inference on covariance structures via the extended cross-data-matrix methodology (Q311815) (← links)
• Sparse-smooth regularized singular value decomposition (Q391596) (← links)
• Correlation tests for high-dimensional data using extended cross-data-matrix methodology (Q391612) (← links)
• PCA consistency for the power spiked model in high-dimensional settings (Q391897) (← links)
• Boundary behavior in high dimension, low sample size asymptotics of PCA (Q432317) (← links)
• A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data (Q741160) (← 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)
• A singular value decomposition of a \(k\)-way array for a principal component analysis of multiway data, \(\text{PTA-}k\) (Q1375538) (← links)
• Inference on high-dimensional mean vectors with fewer observations than the dimension (Q1930608) (← links)
• Projection pursuit via white noise matrices (Q1936426) (← links)
• Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models (Q2000734) (← links)
• Hypothesis tests for high-dimensional covariance structures (Q2042528) (← links)
• Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings (Q2048123) (← links)
• Geometric classifiers for high-dimensional noisy data (Q2062792) (← links)
• Perturbation theory for cross data matrix-based PCA (Q2140856) (← links)
• High dimension low sample size asymptotics of robust PCA (Q2259533) (← links)
• On asymptotic normality of cross data matrix-based PCA in high dimension low sample size (Q2293385) (← links)
• Equality tests of high-dimensional covariance matrices under the strongly spiked eigenvalue model (Q2317309) (← links)
• Inference on high-dimensional mean vectors under the strongly spiked eigenvalue model (Q2329874) (← links)
• Asymptotic normality for inference on multisample, high-dimensional mean vectors under mild conditions (Q2516392) (← links)
• Two-Stage Procedures for High-Dimensional Data (Q3106536) (← links)
• Authors' Response (Q3106538) (← links)
• Statistical inference for high-dimension, low-sample-size data (Q4568290) (← links)
• A High-Dimensional Two-Sample Test for Non-Gaussian Data under a Strongly Spiked Eigenvalue Model (Q4578226) (← links)
• A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise (Q4632469) (← links)
• A survey of high dimension low sample size asymptotics (Q4639812) (← links)
• (Q5011468) (← links)
• Discussion on “Two-Stage Procedures for High-Dimensional Data” by Makoto Aoshima and Kazuyoshi Yata (Q5894438) (← links)
• CORRELATION MATRIX OF EQUI-CORRELATED NORMAL POPULATION: FLUCTUATION OF THE LARGEST EIGENVALUE, SCALING OF THE BULK EIGENVALUES, AND STOCK MARKET (Q6095475) (← links)
• Polynomial whitening for high-dimensional data (Q6178887) (← links)
• Effective methodologies for high-dimensional data (Q6486990) (← links)
• Statistical inference under the strongly spiked eigenvalue model (Q6601514) (← links)
• Test for high-dimensional outliers with principal component analysis (Q6670084) (← links)