· 2015
Statistical Learning with Sparsity
- 2,587citations
- 2015year
Short summary
This book introduces statistical learning methods that leverage sparsity, particularly the Lasso, to effectively handle high-dimensional data by estimating models with few non-zero parameters.
AI-generated from the title and abstract; the full text is not read.
Key points
- Sparse statistical models have a limited number of non-zero parameters.
- Sparsity simplifies the estimation and interpretation of statistical models.
- Methods exploiting sparsity are crucial for dealing with high-dimensional data.
- The Lasso and its generalizations are key techniques presented.
AI-generated from the title and abstract; the full text is not read.
Abstract
Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl
The authors' abstract, as published at the source., 2015 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics