A generalized information criterion for high-dimensional PCA rank selection
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Publication:2165846
DOI10.1007/s00362-021-01276-7OpenAlexW3021407094MaRDI QIDQ2165846
Su-Yun Huang, Hung Hung, Ching-Kang Ing
Publication date: 23 August 2022
Published in: Statistical Papers (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2004.13914
Cites Work
- On model selection from a finite family of possibly misspecified time series models
- On sample eigenvalues in a generalized spiked population model
- Two-stage image denoising by principal component analysis with local pixel grouping
- Spectral analysis of large dimensional random matrices
- Consistency of AIC and BIC in estimating the number of significant components in high-dimensional principal component analysis
- Two-stage dimension reduction for noisy high-dimensional images and application to cryogenic electron microscopy
- Principal manifolds for data visualization and dimension reduction. Reviews and original papers presented partially at the workshop `Principal manifolds for data cartography and dimension reduction', Leicester, UK, August 24--26, 2006.
- Generalised information criteria in model selection
- PCANet: A Simple Deep Learning Baseline for Image Classification?
- Nonsparse Learning with Latent Variables
- Large Sample Covariance Matrices and High-Dimensional Data Analysis
- ON ESTIMATION OF THE POPULATION SPECTRAL DISTRIBUTION FROM A HIGH‐DIMENSIONAL SAMPLE COVARIANCE MATRIX
- Determining the Number of Factors in Approximate Factor Models
- Model Selection Principles in Misspecified Models
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