LEADER 02154nam a2200409 i 4500 001 991000996379707536 005 20020507104916.0 008 010201s1953 us ||| | eng 035 $ab10158960-39ule_inst 035 $aLE00640366$9ExL 040 $aDip.to Fisica$bita 084 $a510(083) 084 $a510.32 084 $a517.5 084 $a518 100 1 $aBateman, Harry$0165 245 10$aHigher transcendental functions :$bbased, in part, on notes left by Harry Bateman, and compiled by the staff of the Bateman Manuscript Project [Director :$bArthur Erdélyi. Research associates :$bWilhelm Magnus, Fritz Oberhettinger, and Francesco G. Tricomi] 260 $aNew York :$bMcGraw-Hill Book Co.,$c1953-55 300 $a3 v. :$bill. ;$c24 cm. 500 $a"Prepared at the California Institute of Technology under contract no. N6onr-244, task order XIV, with the Office of Naval Research. Project designation number: NR 043-045." 500 $aIncludes bibliographies. 650 4$aFunctions (Mathematics) 650 4$aTranscendental functions 700 1 $aErdélyi, Arthur 710 2 $aBateman Manuscript Project 710 2 $aUnited States. Office of Naval Research 907 $a.b10158960$b02-04-14$c27-06-02 912 $a991000996379707536 945 $aLE006 510(083) BAT$cV. 2$g1$i2006000019385$lle006$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10192906$z27-06-02 945 $aLE006 510(083) BAT$cV. 3$g1$i2006000019392$lle006$o-$pE0.00$q-$rl$s- $t0$u2$v0$w2$x0$y.i10192918$z27-06-02 945 $aLE006 510(083) BAT$cV. 3$g1$i2006000019408$lle006$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i1019292x$z27-06-02 945 $aLE006 510(083) BAT$cV. 1$g1$i2006000019422$lle006$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10192931$z27-06-02 945 $aLE006 510(083) BAT$cV. 2$g1$i2006000019439$lle006$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10192943$z27-06-02 945 $aLE006 510(083) BAT$cV. 1$g1$i2006000084116$lle006$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10192955$z27-06-02 996 $aHigher transcendental functions$9187557 997 $aUNISALENTO 998 $ale006$b01-01-01$cm$da $e-$feng$gus $h0$i6 LEADER 05154nam 22006375 450 001 9910437861803321 005 20200701063241.0 010 $a1-4614-6849-3 024 7 $a10.1007/978-1-4614-6849-3 035 $a(CKB)3390000000037144 035 $a(SSID)ssj0000904250 035 $a(PQKBManifestationID)11943813 035 $a(PQKBTitleCode)TC0000904250 035 $a(PQKBWorkID)10908879 035 $a(PQKB)10545081 035 $a(DE-He213)978-1-4614-6849-3 035 $a(MiAaPQ)EBC6245972 035 $a(MiAaPQ)EBC1317001 035 $a(Au-PeEL)EBL1317001 035 $a(CaPaEBR)ebr10969096 035 $a(OCoLC)870244221 035 $a(PPN)170487997 035 $a(EXLCZ)993390000000037144 100 $a20130517d2013 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aApplied Predictive Modeling /$fby Max Kuhn, Kjell Johnson 205 $a1st ed. 2013. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2013. 215 $a1 online resource (XIII, 600 p. 203 illus., 153 illus. in color.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-4614-6848-5 320 $aIncludes bibliographical references (pages 569-587) and index. 327 $aGeneral Strategies -- Regression Models -- Classification Models -- Other Considerations -- Appendix -- References -- Indices. 330 $aThis text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance?all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code for each step of the process. The data sets and corresponding code are available in the book?s companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner?s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book?s R package. Readers and students interested in implementing the methods should have some basic knowledge of R. And a handful of the more advanced topics require some mathematical knowledge. . 606 $aStatistics 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 606 $aStatistics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/S0000X 615 0$aStatistics. 615 14$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aStatistics and Computing/Statistics Programs. 615 24$aStatistics, general. 676 $a519.5 700 $aKuhn$b Max$4aut$4http://id.loc.gov/vocabulary/relators/aut$0524999 702 $aJohnson$b Kjell$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437861803321 996 $aApplied Predictive Modeling$92528198 997 $aUNINA