LDR 03017cam0a2200349   4500
010    _a9783319327730
090    _a16249
101    _aeng
102    _aCH
100    _a20110708              frey50       
200    _aEstimation and testing under sparsity
       _eécole d'été de probabilités de Saint-Flour XLV - 2015
       _fSara van de Geer
210    _aCham
215    _a1 vol. (XIII-274 p.)
225    _aLecture notes in mathematics
       _iécole d'été de probabilités de Saint-Flour
320    _aBibliogr. p. 267-269. Index
330    _aThe book deals with models of high-dimensional data, that is models where the number of parameters to be estimated is larger than the number of observations available for parameter estimation. Nowadays, such models are very important, as due to the significant technological advances large volumes of observations can, and are often recorded (through internet, cameras, smartphones, etc.). In addition, the parameter set may be sparse, that is the number of really relevant parameters is smaller than the number of the observations, but no one knows how many they are beforehand. An important technique when dealing with parameter estimation in such high-dimensional models is the Lasso method. The book uses this method as the starting point and the basis for the understanding of other methods also presented and discussed, such as those inducing structured sparsity or low rank or those based on more general loss functions. The book provides several examples and illustrations of the methods presented and discussed, while each of its 17 chapters ends with a problem section. Thus, it can be used as textbook for students mainly at postgraduate level. (zbMath)
410    _9132706
       _tLecture notes in mathematics
676    _a2010
686    _9165439
       _bProbability theory and stochastic processes
       _xResearch exposition (monographs, survey articles)
686    _9165470
       _bProbability theory and stochastic processes -- Limit theorems
       _xCentral limit and other weak theorems
686    _9165473
       _bProbability theory and stochastic processes -- Limit theorems
       _xFunctional limit theorems; invariance principles
686    _9165630
       _bStatistics -- Linear inference, regression
       _xRidge regression; shrinkage estimators
686    _9165632
       _bStatistics -- Linear inference, regression
       _xGeneralized linear models
701    _4070
       _bSara A. van de
710    _4070
       _aécole d'été de probabilités de Saint-Flour
856    _uhttps://link.springer.com/book/10.1007%2F978-3-319-32774-7
       _zSpringerlink - résumé
856    _uhttps://zbmath.org/?q=an:1362.62006
856    _uhttps://mathscinet.ams.org/mathscinet-getitem?mr=3526202
905    _aaw
906    _aaw
001     16249
995    _f12441-01
       _xachat Ebsco
       _kEcole STF
       _eSalle S
Languages: English | Français | |