Penalized Independence Rule for Testing High-Dimensional Hypotheses
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Publication:3017854
DOI10.1080/03610926.2010.484160zbMath1217.62081OpenAlexW2006719445MaRDI QIDQ3017854
Jun Zhu, Zheng Yan Lin, Yanfeng Shen
Publication date: 20 July 2011
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2010.484160
Ridge regression; shrinkage estimators (Lasso) (62J07) Hypothesis testing in multivariate analysis (62H15) Generalized linear models (logistic models) (62J12)
Uses Software
Cites Work
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- The Adaptive Lasso and Its Oracle Properties
- Regularization in statistics
- High-dimensional classification using features annealed independence rules
- Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
- Significance analysis of microarrays applied to the ionizing radiation response
- Test of Significance Based on Wavelet Thresholding and Neyman's Truncation
- Testing Against a High Dimensional Alternative
- Ideal spatial adaptation by wavelet shrinkage
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- The elements of statistical learning. Data mining, inference, and prediction
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