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Functional Data Analysis with R and MATLAB / by James Ramsay, Giles Hooker, Spencer Graves.

By: Ramsay, James [autor]Contributor(s): Springerlink (Online Service)Material type: TextTextLanguage: English Series: Use RPublisher: New York, NY : Springer New York, 2009Publisher: [California] : [s.e.], 2009Description: online resource. : ilustracionesContent type: texto Media type: computadora Carrier type: recurso en líneaISBN: 9780387981840; 9780387981857; 99780387981857Subject(s): MATLAB | Biometría  | Análisis Multivalente -- Procesamiento de datos | Diseño Experimental | Estadística -- cuadros, Tablas, Etc | Psicometría | Mercadeo | Análisis de datos | Salud pública | Estadística matemática | Minería de datosDDC classification: 519.5 Online resources: Recurso digital
Contents:
Introduction to 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.
Abstract: Scientists 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.
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Introduction to 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.

Scientists 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.

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