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＜電子ブック＞
S+Functional Data Analysis : User's Manual for Windows ® / by Douglas B. Clarkson, Chris Fraley, Charles Gu, James Ramsay

版	1st ed. 2005.
出版者	New York, NY : Springer New York : Imprint: Springer
出版年	2005
本文言語	英語
大きさ	X, 192 p : online resource
著者標目	*Clarkson, Douglas B author Fraley, Chris author Gu, Charles author Ramsay, James author SpringerLink (Online service)
件　名	LCSH:Mathematical statistics -- Data processing  全ての件名で検索 LCSH:Statistics  LCSH:Biometry FREE:Statistics and Computing FREE:Statistical Theory and Methods FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences FREE:Biostatistics
一般注記	Basis 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 S+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 HTTP:URL=https://doi.org/10.1007/0-387-28393-5

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