Pages that link to "Item:Q391897"

The following pages link to PCA consistency for the power spiked model in high-dimensional settings (Q391897):

Displaying 39 items.

• Asymptotic properties of principal component analysis and shrinkage-bias adjustment under the generalized spiked population model (Q131450) (← links)
• ``Virus hunting'' using radial distance weighted discrimination (Q262396) (← links)
• Reconstruction of a high-dimensional low-rank matrix (Q276225) (← links)
• Asymptotic properties of the first principal component and equality tests of covariance matrices in high-dimension, low-sample-size context (Q899373) (← links)
• PCA consistency in high dimension, low sample size context (Q1043724) (← links)
• A \(U\)-classifier for high-dimensional data under non-normality (Q1661350) (← links)
• Consistency of sparse PCA in high dimension, low sample size contexts (Q1941448) (← links)
• Linear components of quadratic classifiers (Q1999446) (← links)
• Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models (Q2000734) (← links)
• Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings (Q2023463) (← links)
• On the asymptotic normality and efficiency of Kronecker envelope principal component analysis (Q2034476) (← links)
• Geometric classifiers for high-dimensional noisy data (Q2062792) (← links)
• Asymptotic properties of distance-weighted discrimination and its bias correction for high-dimension, low-sample-size data (Q2068931) (← links)
• Consistency of the objective general index in high-dimensional settings (Q2078579) (← links)
• Shrinkage priors for single-spiked covariance models (Q2244463) (← links)
• (Q2283662) (redirect page) (← 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)
• Location-invariant tests of homogeneity of large-dimensional covariance matrices (Q2321809) (← links)
• A general framework for consistency of principal component analysis (Q2834478) (← links)
• PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context (Q3644996) (← 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 survey of high dimension low sample size asymptotics (Q4639812) (← links)
• Detecting changes in the second moment structure of high-dimensional sensor-type data in a <i>K</i>-sample setting (Q4965652) (← links)
• SURE estimates for high dimensional classification (Q4970345) (← links)
• (Q5004041) (← links)
• (Q5011468) (← links)
• A classifier under the strongly spiked eigenvalue model in high-dimension, low-sample-size context (Q5077372) (← links)
• Robust support vector machine for high-dimensional imbalanced data (Q5082626) (← links)
• A model of double descent for high-dimensional binary linear classification (Q5095258) (← links)
• Quadratic Discriminant Analysis for High-Dimensional Data (Q5226616) (← links)
• On Estimation of the Noise Variance in High Dimensional Probabilistic Principal Component Analysis (Q5378155) (← links)
• Sparse quadratic classification rules via linear dimension reduction (Q6032761) (← links)
• Minimum cost‐compression risk in principal component analysis (Q6075174) (← links)
• Polynomial whitening for high-dimensional data (Q6178887) (← links)
• Asymptotic properties of multiclass support vector machine under high dimensional settings (Q6562746) (← links)
• Statistical inference under the strongly spiked eigenvalue model (Q6601514) (← links)
• Test for high-dimensional outliers with principal component analysis (Q6670084) (← links)