LEADER 01062nam a2200277 i 4500 001 991000516309707536 005 20020503182323.0 008 940524s1987 us ||| | eng 020 $a0471810339 035 $ab10089081-39ule_inst 035 $aLE02517484$9ExL 040 $aFac. Economia$bita 082 0 $a519.5 100 1 $aBox, George E.P.$030397 245 10$aEmpirical model-building and response surfaces /$cGeorge E.P. Box, Norman R. Draper 260 $aNew York :$bWiley & Sons,$cc1987 300 $axiv, 669 p. ;$c23 cm. 490 0 $aWiley series in probability and mathematical statistics. Applied probability and statistics 650 4$aStatistica matematica 700 1 $aDraper, Norman R. 907 $a.b10089081$b17-02-17$c27-06-02 912 $a991000516309707536 945 $aLE025 ECO 519.5 BOX01.03$g1$i2025000018518$lle025$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10103296$z27-06-02 996 $aEmpirical model-building and response surfaces$9195940 997 $aUNISALENTO 998 $ale025$b01-01-94$cm$da $e-$feng$gus $h0$i1 LEADER 05414nam 22008175 450 001 9910254081103321 005 20250505002606.0 010 $a3-319-27099-0 024 7 $a10.1007/978-3-319-27099-9 035 $a(CKB)3710000000602301 035 $a(EBL)4409678 035 $a(SSID)ssj0001653886 035 $a(PQKBManifestationID)16432841 035 $a(PQKBTitleCode)TC0001653886 035 $a(PQKBWorkID)14982765 035 $a(PQKB)11371470 035 $a(DE-He213)978-3-319-27099-9 035 $a(MiAaPQ)EBC4409678 035 $a(PPN)192222465 035 $a(EXLCZ)993710000000602301 100 $a20160216d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical Analysis for High-Dimensional Data $eThe Abel Symposium 2014 /$fedited by Arnoldo Frigessi, Peter Bühlmann, Ingrid Glad, Mette Langaas, Sylvia Richardson, Marina Vannucci 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (313 p.) 225 1 $aAbel Symposia,$x2197-8549 ;$v11 300 $aDescription based upon print version of record. 311 08$a3-319-27097-4 327 $aSome Themes in High-Dimensional Statistics: A. Frigessi et al -- Laplace Appoximation in High-Dimensional Bayesian Regression: R. Barber, M. Drton et al -- Preselection in Lasso-Type Analysis for Ultra-High Dimensional Genomic Exploration: L.C. Bergersen, I. Glad et al -- Spectral Clustering and Block Models: a Review and a new Algorithm: S. Bhattacharyya et al -- Bayesian Hierarchical Mixture Models: L. Bottelo et al -- iBATCGH; Integrative Bayesian Analysis of Transcriptomic and CGH Data: Cassese, M. Vannucci et al -- Models of Random Sparse Eigenmatrices and Bayesian Analysis of Multivariate Structure: A.J. Cron, M. West -- Combining Single and Paired End RNA-seq Data for Differential Expression Analysis: F. Feng, T.Speed et al -- An Imputation Method for Estimation the Learning Curve in Classification Problems: E. Laber et al -- Baysian Feature Allocation Models for Tumor Heterogeneity: J. Lee, P. Mueller et al -- Bayesian Penalty Mixing: The Case of a Non-Separable Penalty: V. Rockova etal -- Confidence Intervals for Maximin Effects in Inhomogeneous Large Scale Data: D. Rothenhausler et al -- Chisquare Confidence Sets in High-Dimensional Regression: S. van de Geer et al. . 330 $aThis book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvċgar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in ?big data? situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community. 410 0$aAbel Symposia,$x2197-8549 ;$v11 606 $aMathematics$xData processing 606 $aStatistics 606 $aBioinformatics 606 $aMathematical statistics$xData processing 606 $aBiometry 606 $aStatistics 606 $aComputational Mathematics and Numerical Analysis 606 $aStatistical Theory and Methods 606 $aBioinformatics 606 $aStatistics and Computing 606 $aBiostatistics 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 615 0$aMathematics$xData processing. 615 0$aStatistics. 615 0$aBioinformatics. 615 0$aMathematical statistics$xData processing. 615 0$aBiometry. 615 0$aStatistics. 615 14$aComputational Mathematics and Numerical Analysis. 615 24$aStatistical Theory and Methods. 615 24$aBioinformatics. 615 24$aStatistics and Computing. 615 24$aBiostatistics. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 676 $a510 702 $aFrigessi$b Arnoldo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBühlmann$b Peter$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGlad$b Ingrid$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLangaas$b Mette$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRichardson$b Sylvia$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aVannucci$b Marina$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910254081103321 996 $aStatistical analysis for high-dimensional data$91523641 997 $aUNINA