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Bi-cross-validation for factor analysis (Q104117)

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scientific article; zbMATH DE number 6946215

Language	Label	Description	Also known as
English	Bi-cross-validation for factor analysis	scientific article; zbMATH DE number 6946215

Statements

scholarly article

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publication date

11 March 2015

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2 October 2018

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arXiv classification

stat.ME

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MaRDI profile type

MaRDI publication profile

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Bi-cross-validation for factor analysis (English)

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Statistical Science

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full work available at URL

https://arxiv.org/abs/1503.03515

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https://projecteuclid.org/euclid.ss/1455115917

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zbMATH Keywords

parallel analysis

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random matrix theory

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scree plot

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unwanted variation

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Eigenvalue Ratio Test for the Number of Factors

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Improved penalization for determining the number of factors in approximate factor models

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Statistical analysis of factor models of high dimension

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Determining the Number of Factors in Approximate Factor Models

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Eigenvalues of large sample covariance matrices of spiked population models

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The singular values and vectors of low rank perturbations of large rectangular random matrices

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Latent variable graphical model selection via convex optimization

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

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Detection of signals by information theoretic criteria: general asymptotic performance analysis

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Equivalence of regularization and truncated iteration in the solution of ill-posed image reconstruction problems

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The Generalized Dynamic Factor Model

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The Optimal Hard Threshold for Singular Values is <inline-formula> <tex-math notation="TeX">\(4/\sqrt {3}\) </tex-math></inline-formula>

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Determining the Number of Factors in the General Dynamic Factor Model

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A review of signal subspace speech enhancement and its application to noise robust speech recognition

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A rationale and test for the number of factors in factor analysis

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Principal component analysis.

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A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets

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Factor modeling for high-dimensional time series: inference for the number of factors

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TESTS OF SIGNIFICANCE FOR THE LATENT ROOTS OF COVARIANCE AND CORRELATION MATRICES

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A general framework for multiple testing dependence

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OptShrink: An Algorithm for Improved Low-Rank Signal Matrix Denoising by Optimal, Data-Driven Singular Value Shrinkage

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Sample Eigenvalue Based Detection of High-Dimensional Signals in White Noise Using Relatively Few Samples

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Finite sample approximation results for principal component analysis: A matrix perturbation approach

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Asymptotics of the principal components estimator of large factor models with weakly influential factors

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Asymptotic analysis of the squared estimation error in misspecified factor models

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Bi-cross-validation of the SVD and the nonnegative matrix factorization

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Bi-cross-validation for factor analysis

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How many principal components? Stopping rules for determining the number of non-trivial axes revisited

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On a Heuristic Method of Test Construction and its use in Multivariate Analysis

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Multiple hypothesis testing adjusted for latent variables, with an application to the AGEMAP gene expression data

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Determining the number of components from the matrix of partial correlations

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On early stopping in gradient descent learning

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Boosting with early stopping: convergence and consistency

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Identifiers

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zbMATH Open document ID

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Mathematics Subject Classification ID

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zbMATH DE Number

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10.48550/ARXIV.1503.03515

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10.1214/15-STS539

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Sitelinks

Mathematics(1 entry)

mardi Publication:104117

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