04114nam a22005055i 4500001001800000003000900018005001700027007001500044008004100059020001800100020001900118024003500137082001400172100002800186245012000214264004600334300002100380336002600401337002600427338003600453347002400489490001000513505052600523520198301049650001603032650001703048650002503065650002903090650001503119650001903134650001603153650005003169650004103219650001503260650001903275650001903294650003603313700002803349700003003377710003403407773002003441776003603461830001003497856010103507978-0-387-98185-7DE-He21320260521092024.0cr nn 008mamaa100301s2009    xxu|    s    |||| 0|eng d  a9780387981857  a997803879818577 a10.1007/978-0-387-98185-72doi04a519.52231 aRamsay, James.eauthor.10aFunctional Data Analysis with R and MATLABh[electronic resource] /cby James Ramsay, Giles Hooker, Spencer Graves. 1aNew York, NY :bSpringer New York,c2009.  bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aUse R0 ato Functional Data Analysis -- Essential Comparisons of the Matlab and R Languages -- How to Specify Basis Systems for Building Functions -- How to Build Functional Data Objects -- Smoothing: Computing Curves from Noisy Data -- Descriptions of Functional Data -- Exploring Variation: Functional Principal and Canonical Components Analysis -- Registration: Aligning Features for Samples of Curves -- Functional Linear Models for Scalar Responses -- Linear Models for Functional Responses -- Functional Models and Dynamics.  aScientists often collect samples of curves and other functional observations, and develop models where parameters are also functions. This volume in the UseR! Series is aimed at a wide range of readers, and especially those who would like apply these techniques to their research problems. It complements Functional Data Analysis, Second Edition and Applied Functional Data Analysis: Methods and Case Studies by providing computer code in both the R and Matlab languages for a set of data analyses that showcase functional data analysis techniques. The authors make it easy to get up and running in new applications by adapting the code for the examples, and by being able to access the details of key functions within these pages. This book is accompanied by additional web-based support at http://www.functionaldata.org for applying existing functions and developing new ones in either language. The companion 'fda' package for R includes script files to reproduce nearly all the examples in the book including all but one of the 76 figures. Jim Ramsay is Professor Emeritus at McGill University and is an international authority on many aspects of multivariate analysis. He was President of the Statistical Society of Canada in 2002-3 and holds the Society's Gold Medal for his work in functional data analysis. His statistical work draws on his collaboration with researchers in biomechanics, chemical engineering, climatology, ecology, economics, human biology, medicine and psychology. Giles Hooker is Assistant Professor of Biological Statistics and Computational Biology at Cornell University. His research interests include statistical inference in nonlinear dynamics, machine learning and computational statistics. Spencer Graves is an engineer with a PhD in Statistics and over 15 years experience using S-Plus and R to analyze data in a broad range of applications. He has made substantive contributions to several CRAN packages including 'fda' and 'DierckxSpline.'  0aSTATISTICS. 0aDATA MINING. 0aSTATISTICAL METHODS. 0aMATHEMATICAL STATISTICS. 0aMARKETING. 0aPSYCHOMETRICS.14aSTATISTICS.24aSTATISTICS AND COMPUTING/STATISTICS PROGRAMS.24aDATA MINING AND KNOWLEDGE DISCOVERY.24aMARKETING.24aBIOSTATISTICS.24aPSYCHOMETRICS.24aPUBLIC HEALTH/GESUNDHEITSWESEN.1 aHooker, Giles.eauthor.1 aGraves, Spencer.eauthor.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387981840 0aUse R40uhttp://dx.doi.org/10.1007/978-0-387-98185-7zVer el texto completo en las instalaciones del CICY