- Author
- Górecki Tomasz (Adam Mickiewicz University in Poznań, Poland), Krzyśko Mirosław (Adam Mickiewicz University in Poznań, Poland), Waszak Łukasz (Adam Mickiewicz University in Poznań, Poland), Wołyński Waldemar (Adam Mickiewicz University in Poznań, Poland)
- Title
- Methods of Reducing Dimension for Functional Data
- Source
- Statistics in Transition, 2014, vol. 15, nr 2, s. 231-242, rys., tab., bibliogr. 10 poz.
- Keyword
- Wielowymiarowa analiza statystyczna, Metody statystyczne
Multi-dimensional statistical analysis, Statistical methods - Note
- summ.
- Abstract
- In classical data analysis, objects are characterized by many features observed at one point of time. We would like to present them graphically, to see their configuration, eliminate outlying observations, observe relationships between them or to classify them. In recent years methods for representing data by functions have received much attention. In this paper we discuss a new method of constructing principal components for multivariate functional data. We illustrate our method with data from environmental studies. (original abstract)
- Accessibility
- The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
The Main Library of the Wroclaw University of Economics - Full text
- Show
- Bibliography
-
- BESSE, P., (1979). Etude descriptive d'un processus. Ph.D. thesis, Université Paul Sabatier.
- FISHER, R. A., (1936). The use of multiple measurements in taxonomic problem, Annals of Eugenics 7, 179-188.
- GÓRECKI, T., KRZYŚKO, M., (2012). Functional Principal Components Analysis, Data analysis methods and its applications, C.H. Beck 71-87.
- GÓRECKI, T., KRZYŚKO, M., WASZAK, Ł., (2014). Functional Discriminant Coordinates, Communication in Statistics - Theory and Methods 43(5), 1013-1025.
- JACQUES, J., PREDA, C., (2014). Model-based clustering for multivariate functional data, Computational Statistics & Data Analysis 71, 92-106.
- JOLLIFFE, I. T., (2002). Principal Component Analysis, Second Edition, Springer.
- KRZYŚKO, M., WASZAK, Ł., (2013). Canonical correlation analysis for functional data, Biometrical Letters 50(2), 95-105.
- RAMSAY, J. O., SILVERMAN, B. W., (2005). Functional Data Analysis, Second Edition, Springer.
- SAPORTA, G., (1981). Methodes exploratoires d'analyse de données temporelles. Cahiers du Buro. Ph.D. thesis.
- SHMUELI, G., (2010). To explain or to predict? Statistical Science 25(3), 289-310.
- Cited by
- ISSN
- 1234-7655
- Language
- eng
