Discriminant Coordinates Analysis in the Case of Multivariate Repeated Data

• Autorzy: Mirosław Krzyśko , Wojciech Łukaszonek , Waldemar Wołyński
• Języki publikacji: EN

Abstrakty

EN

The main aim of the paper is to adapt the classical discriminant coordinates analysis to multivariate repeated measures data. The proposed solution is based on the relationship between the discriminant coordinates and canonical variables. The quality of these new discriminant coordinates is examined on some real data. (original abstract)

Słowa kluczowe

EN

Discriminant analysis; Multi-dimensional comparative analysis; Taxonomic methods; Comparative spatial analysis

PL

Analiza dyskryminacyjna; Wielowymiarowa analiza porównawcza; Metody taksonomiczne; Przestrzenna analiza porównawcza

• Czasopismo: Statistics in Transition
• Rocznik: 2018
• Tom: 19
• Numer: nr 3
• Strony: 495--506

Twórcy

autor

Mirosław Krzyśko

• Adam Mickiewicz University in Poznań, Poland

autor

Wojciech Łukaszonek

• President Wojciechowski Higher Vocational State School, Poland

autor

Waldemar Wołyński

• Adam Mickiewicz University in Poznań, Poland

Bibliografia

• BANERJEE, S., ROY, A., (2014), Linear Algebra and Matrix Analysis for Statistics. CRC Press, Boca Ratop.
• FISHER, R. A., (1936), The use of multiple measurements in taxonomic problem. Annals of Eugenics 7, pp. 179-188.
• FUJIKOSHI, Y., ULYANOV, V. V., SHIMIZU, R., (2010), Multivariate statistics. High Dimensional and Large-Sample Approximations. Hoboken, New Jersey.
• GIRI, C. G., (1996), Multivariate Statistical Analysis. Marcel Dekker, Inc., New York.
• GÓRECKI, T., KRZYŚKO, M., WASZAK, Ł., WOŁYŃSKI, W., (2018), Selected statistical methods of data analysis for multivariate functional data, Statistical Papers 59, pp. 153-182.
• KRZANOWSKI, W. J., (2000), Principles of Multivariate Analysis, Revised Edition. Oxford University Press, Oxford.
• KRZYŚKO, M., (1979), Discriminant variables. Biometrical Journal 21 (3), pp. 227-241.
• RAO, C. R., (1948), The utilization of multiple measurements in problem of biological classification. Journal of the Royal Statistical Society Series B, 10 (2), pp. 159-203.
• R CORE TEAM, (2015), R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.Rproject.org/.
• RENCHER, A. C., (1998), Multivariate Statistical Inference and Applications. Wiley, New York.
• SEBER, G. A. F., (1984), Multivariate Observations. John Wiley and Sons, New York.
• SRIVASTAVA, M. S., (2002), Methods of Multivariate Statistics. Wiley, New York.
• SRIVASTAVA, M. S., VON ROSEN, T., VON ROSEN, D., (2008), Models with a Kronecker product covariance structure: estimation and testing. Mathematical Methods of Statistics 17 (4), pp. 357-370.

Identyfikatory

DOI

10.21307/stattrans-2018-027