000 03419nam a22004575i 4500
001 978-0-387-28393-7
003 DE-He213
005 20250710083941.0
007 cr nn 008mamaa
008 100301s2005 xxu| s |||| 0|eng d
020 _a9780387283937
_a99780387283937
024 7 _a10.1007/0-387-28393-5
_2doi
082 0 4 _a519.5
_223
100 1 _aClarkson, Douglas B.
_eauthor.
245 1 0 _aS+ Functional Data Analysis
_h[recurso electrónico] :
_bUser's Manual for Windows® /
_cby Douglas B. Clarkson, Chris Fraley, Charles C. Gu, James O. Ramsey.
264 1 _aNew York, NY :
_bSpringer New York,
_c2005.
300 _aX, 192 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aBasis Objects and Operations -- Functional Data Objects and Operations -- Linear Differential Operators and Smoothing -- Functional Registration -- Functional Linear Models -- Functional Generalized Linear Models -- Functional Principal Components -- Canonical Correlation -- Functional Cluster Analysis -- Principal Differential Analysis.
520 _aS+Functional Data Analysis is the first commercial object oriented package for exploring, modeling, and analyzing functional data. Functional data analysis (FDA) handles longitudinal data and treats each observation as a function of time (or other variable). The functions are related. The goal is to analyze a sample of functions instead of a sample of related points. FDA differs from traditional data analytic techniques in a number of ways. Functions can be evaluated at any point in their domain. Derivatives and integrals, which may provide better information (e.g. graphical) than the original data, are easily computed and used in multivariate and other functional analytic methods. The analyst using S+FDA can handle irregularly spaced data or data with missing values. For large amounts of data, working with a functional representation can save storage. Moreover, S+FDA provides a variety of analytic techniques for functional data including linear models, generalized linear models, principal components, canonical correlation, principal differential analysis, and clustering. This book can be considered a companion to two other highly acclaimed books involving James Ramsay and Bernard Silverman: Functional Data Analysis, Second Edition (2005) and Applied Functional Data Analysis (2002). This user's manual also provides the documentation for the S+FDA library for S­Plus.
650 0 _aSTATISTICS.
650 0 _aMATHEMATICAL STATISTICS.
650 1 4 _aSTATISTICS.
650 2 4 _aSTATISTICS AND COMPUTING/STATISTICS PROGRAMS.
650 2 4 _aSTATISTICAL THEORY AND METHODS.
650 2 4 _aSTATISTICS FOR ENGINEERING, PHYSICS, COMPUTER SCIENCE, CHEMISTRY & GEOSCIENCES.
650 2 4 _aSTATISTICS FOR LIFE SCIENCES, MEDICINE, HEALTH SCIENCES.
700 1 _aFraley, Chris.
_eauthor.
700 1 _aGu, Charles C.
_eauthor.
700 1 _aRamsey, James O.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780387249698
856 4 0 _uhttp://dx.doi.org/10.1007/0-387-28393-5
_zVer el texto completo en las instalaciones del CICY
912 _aZDB-2-SMA
942 _2ddc
_cER
999 _c56821
_d56821