LDR 03017cam0a2200349   4500
010    _a9783319327730
       _bbr.
090    _a16249
101    _aeng
102    _aCH
100    _a20110708              frey50       
200    _aEstimation and testing under sparsity
       _bCONG
       _eécole d'été de probabilités de Saint-Flour XLV - 2015
       _fSara van de Geer
210    _aCham
       _cSpringer
       _d2016
215    _a1 vol. (XIII-274 p.)
       _cfig.
       _d24
225    _aLecture notes in mathematics
       _v2159
       _9168961
       _x0075-8434
       _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
       _aSpringer
       _tLecture notes in mathematics
       _x0075-8434
676    _a2010
686    _9165439
       _a60-02
       _bProbability theory and stochastic processes
       _xResearch exposition (monographs, survey articles)
       _20
686    _9165470
       _a60F05
       _bProbability theory and stochastic processes -- Limit theorems
       _xCentral limit and other weak theorems
       _20
686    _9165473
       _a60F17
       _bProbability theory and stochastic processes -- Limit theorems
       _xFunctional limit theorems; invariance principles
       _20
686    _9165630
       _a62J07
       _bStatistics -- Linear inference, regression
       _xRidge regression; shrinkage estimators
       _20
686    _9165632
       _a62J12
       _bStatistics -- Linear inference, regression
       _xGeneralized linear models
       _20
701    _4070
       _aGeer
       _bSara A. van de
       _f1958-
       _9183118
710    _4070
       _9167607
       _aécole d'été de probabilités de Saint-Flour
       _d45
       _eSaint-Flour
       _f2015
856    _uhttps://link.springer.com/book/10.1007%2F978-3-319-32774-7
       _zSpringerlink - résumé
856    _uhttps://zbmath.org/?q=an:1362.62006
       _zzbMath
856    _uhttps://mathscinet.ams.org/mathscinet-getitem?mr=3526202
       _zMSN
905    _aaw
       _b2017
906    _aaw
       _b2017-12-15
001     16249
995    _f12441-01
       _xachat Ebsco
       _918557
       _cCMI
       _20
       _kEcole STF
       _o0
       _eSalle S
       _z37
       _bCMI
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