LEADER 01005cam0-22003611i-450 001 990004697920403321 005 20230117091721.0 010 $a2-07-026999-X 035 $a000469792 035 $aFED01000469792 035 $a(Aleph)000469792FED01 100 $a19990604d1969----km-y0itay50------ba 101 0 $afre 102 $aFR 105 $ay-------001yy 200 1 $a<>archéologie du savoir$fMichel Foucault 210 $aParis$cGallimard$d1969 215 $a275 p.$d23 cm 225 1 $aBibliothèque des sciences humaines$v2 610 0 $aSapere 676 $a121$v20 676 $a195$v20 700 1$aFoucault,$bMichel$f<1926-1984>$0124914 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990004697920403321 952 $aLEPORE 266$bLEPORE 266$fFARBC 952 $aP.1 9F FOU 16$bIst.Fil.Cl.264$fFLFBC 952 $aP.1 9F FOU 12$bIST.FIL.T. 3560$fFLFBC 959 $aFARBC 959 $aFLFBC 996 $aArchéologie du savoir$921498 997 $aUNINA LEADER 04846nam 22006735 450 001 9910254293703321 005 20250414094853.0 010 $a3-319-41573-5 024 7 $a10.1007/978-3-319-41573-4 035 $a(CKB)3710000001124600 035 $a(DE-He213)978-3-319-41573-4 035 $a(MiAaPQ)EBC4827775 035 $a(PPN)19976753X 035 $a(EXLCZ)993710000001124600 100 $a20170321d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBig and Complex Data Analysis $eMethodologies and Applications /$fedited by S. Ejaz Ahmed 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XIV, 386 p. 85 illus., 55 illus. in color.) 225 1 $aContributions to Statistics,$x2628-8966 311 08$a3-319-41572-7 320 $aIncludes bibliographical references. 327 $aPreface -- Introduction -- Unsupervised Bump Hunting Using Principal Components -- Statistical Process Control Charts as a Tool for Analyzing Big Data -- Empirical Likelihood Test for High Dimensional Generalized Linear Models -- Identifying gene-environment interactions associated with prognosis using penalized quantile regression -- A Computationally Efficient Approach for Modeling Complex and Big Survival Data -- Regularization after marginal learning for ultra-high dimensional regression models -- Tests of concentration for low-dimensional and high-dimensional directional data -- Random Projections For Large-Scale Regression -- How Different are Estimated Genetic Networks of Cancer Subtypes? -- Analysis of correlated data with error-prone response under generalized linear mixed models -- High-Dimensional Classification for Brain Decoding -- Optimal shrinkage estimation in heteroscedastic hierarchical linear models -- Bias-reduced moment estimators of Population Spectral Distribution and their applications -- Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values -- A Mixture of Variance-Gamma Factor Analyzers -- Fast Community Detection in Complex Networks with a K-Depths Classifier. 330 $aThis volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers. 410 0$aContributions to Statistics,$x2628-8966 606 $aStatistics 606 $aMathematical statistics$xData processing 606 $aQuantitative research 606 $aBiometry 606 $aData mining 606 $aStatistical Theory and Methods 606 $aStatistics and Computing 606 $aData Analysis and Big Data 606 $aBiostatistics 606 $aData Mining and Knowledge Discovery 615 0$aStatistics. 615 0$aMathematical statistics$xData processing. 615 0$aQuantitative research. 615 0$aBiometry. 615 0$aData mining. 615 14$aStatistical Theory and Methods. 615 24$aStatistics and Computing. 615 24$aData Analysis and Big Data. 615 24$aBiostatistics. 615 24$aData Mining and Knowledge Discovery. 676 $a005.7 702 $aAhmed$b S. Ejaz$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254293703321 996 $aBig and Complex Data Analysis$91562233 997 $aUNINA