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Titlebook: Estimation and Testing Under Sparsity; école d‘été de Proba Sara van de Geer Book 2016 Springer International Publishing Switzerland 2016 6

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發(fā)表于 2025-3-21 19:33:40 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Estimation and Testing Under Sparsity
副標(biāo)題école d‘été de Proba
編輯Sara van de Geer
視頻videohttp://file.papertrans.cn/316/315781/315781.mp4
概述Starting with the popular Lasso method as its prime example, the book then extends to a broad family of estimation methods for high-dimensional data.A theoretical basis for sparsity-inducing methods i
叢書名稱Lecture Notes in Mathematics
圖書封面Titlebook: Estimation and Testing Under Sparsity; école d‘été de Proba Sara van de Geer Book 2016 Springer International Publishing Switzerland 2016 6
描述Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. It also provides a semi-parametric approach to establishing confidence intervals and tests. Sparsity-inducing methods have proven to be very useful in the analysis of high-dimensional data. Examples include the Lasso and group Lasso methods, and the least squares method with other norm-penalties, such as the nuclear norm. The illustrations provided include generalized linear models, density estimation, matrix completion and sparse principal components. Each chapter ends with a problem section. The book can be used as a textbook for a graduate or PhD course.
出版日期Book 2016
關(guān)鍵詞62-XX; 60-XX, 68Q87; high-dimensional statistics; sparsity; empirical risk minimization; oracle inequali
版次1
doihttps://doi.org/10.1007/978-3-319-32774-7
isbn_softcover978-3-319-32773-0
isbn_ebook978-3-319-32774-7Series ISSN 0075-8434 Series E-ISSN 1617-9692
issn_series 0075-8434
copyrightSpringer International Publishing Switzerland 2016
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發(fā)表于 2025-3-21 22:45:49 | 只看該作者
Book 2016egularizers. It also provides a semi-parametric approach to establishing confidence intervals and tests. Sparsity-inducing methods have proven to be very useful in the analysis of high-dimensional data. Examples include the Lasso and group Lasso methods, and the least squares method with other norm-
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978-3-319-32773-0Springer International Publishing Switzerland 2016
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Estimation and Testing Under Sparsity978-3-319-32774-7Series ISSN 0075-8434 Series E-ISSN 1617-9692
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發(fā)表于 2025-3-22 17:07:32 | 只看該作者
https://doi.org/10.1007/978-1-4757-4893-2mensional mainly due to the easy way to record or obtain data using the internet, or cameras, or new biomedical technologies, or shopping cards, etc. High-dimensional data can also be “constructed” from only a few variables by considering for example second, third, and higher order interactions.
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Lecture Notes in Mathematicshttp://image.papertrans.cn/e/image/315781.jpg
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發(fā)表于 2025-3-23 09:07:13 | 只看該作者
https://doi.org/10.1007/978-1-4757-4893-2mensional mainly due to the easy way to record or obtain data using the internet, or cameras, or new biomedical technologies, or shopping cards, etc. High-dimensional data can also be “constructed” from only a few variables by considering for example second, third, and higher order interactions.
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