Obtaining information from time data statistical analysis in human component system studies. I: Methods and performances. (English) Zbl 1002.62104

Summary: This article states the problem of time data exploration as the succession of the many analysis paths taken prior to obtaining results, results which are initially in a latent state. The initial data – obtained by an experiment or an observation design – are placed within a hyperparallelepiped HP0 where the directions correspond to factors established a priori and each cell contains a multidimensional signal. An analysis path is thus considered to be a progressive information transformation of the time data including up to five stages – data characterizing, scale transformation, data shaping, statistical analysis method application and result presentation.
For each stage, a nonexhaustive set of methods is proposed. To assess the performance of each stage, several methods are suggested. More particularly, the evaluation of the first stage is considered either in terms of a cardinality reduction between the input and output hyperparallelepipeds HP0 and HP1 or in terms of distance comparisons between the cells of HP0 and HP1. A discussion of the main statistical behaviors encountered in the literature is included.


62P99 Applications of statistics
62-07 Data analysis (statistics) (MSC2010)
Full Text: DOI


[1] Anscombe, F.J., Graphs in statistical analysis, Am. stat., 27, 2, 17-21, (1973)
[2] H. Barreau, Temps, in: Encyclopaedia Universalis, Paris, 1989
[3] Benzecri, J.P., Correspondence analysis handbook, (1992), Marcel Dekker New York · Zbl 0766.62034
[4] Berndt, D.J.; Clifford, J., Finding patterns in time series: a dynamic programming approach, (), 229-248
[5] Bhattacharya, P.; Mukherjee, N.P., On the representation of uncertain information by multidimensional arrays, IEEE trans. syst. man. cybern., 24, 1, 107-111, (1994)
[6] Bouguettaya, A.; Le Viet, Q., Data clustering analysis in a multidimensional space, Inf. sci., 112, 267-295, (1998)
[7] Chaudhuri, D.; Mauthy, C.A.; Chaudhuri, B.B., Finding a subset of representative points in a data set, IEEE trans. syst. man cybern., 24, 9, 1416-1424, (1994)
[8] Chentsov, N.N., Information distance, Encyclopaedia of mathematics, (1990), Kluwer Academic Publishers Dordrecht, MA
[9] F. Dazy, J.F. Le Barzic, L’analyse des données évolutives, Méthodes et applications, Editions Technip, Paris, 1996 · Zbl 0867.62052
[10] A. Di Ciaccio, G. Bove, A factorial method for the analysis of three-way data in tensor spaces, in: Fifth International Symposium on Data Analysis and Informatics, 29 September-2 October 1987, pp. 137-145
[11] Diday, E.; Lemaire, J.; Pouget, J.; Testu, F., Eléments d’analyse des données, (1982), Dunod Paris
[12] Dudewicz, E.J.; Mishra, S.N., Modern mathematical statistics, (1988), Wiley New York · Zbl 0708.62003
[13] Fayyad, U.M.; Piatetski-Shapiro, G.; Smith, P., From data mining to knowledge discovery: an overview, (), 1-34
[14] Gillan, D.J.; Wickens, C.D.; Hollands, J.G.; Carswell, M., Guidelines for presenting quantitative data in HFES publications, Hum. factors, 40, 1, 28-41, (1998)
[15] Jiang, J.; Pick, R.A.; Klein, G., Are you sure you want to use that data in your study?, IEEE trans. syst. man cybern., 25, 2, 378-380, (1994)
[16] Johnson, R.A.; Wichern, D.W., Applied multivariate statistical analysis, (1992), Prentice-Hall Englewoods Cliffs, NJ · Zbl 0745.62050
[17] Loslever, P.; Bouilland, S., Marriage of fuzzy sets and multiple correspondence analysis. examples with subjective interval data and biomedical signals, Fuzzy set syst., 107, 255-275, (1999) · Zbl 0934.62063
[18] Mc Gee, V.E., Principles of statistics, traditional and Bayesian, (1971), Meredith-Corporation New-York
[19] Manton, K.G.; Woodbury, M.A.; Tolley, H.D., Statistical application using fuzzy sets, (1994), Wiley New York · Zbl 0811.62003
[20] Mead, R., The design of experiments: statistical principles for practical application, (1988), Cambridge University Press Cambridge
[21] Pandit, S.M.; Wu, S., Time series and system analysis with applications, (1993), Krieger Malabar, FL
[22] Y.V. Prokhorov, Information, in: Encyclopaedia of Mathematics, Kluwer Academic Publishers, Dordrecht, MA, 1990
[23] Y.V. Prokhorov, Information correlation coefficient, in: Encyclopaedia of Mathematics, Kluwer Academic Publishers, Dordrecht, MA, 1990
[24] Rice, J.A., Mathematical statistics and data analysis, (1995), Duxbury Press Belmont, CA · Zbl 0868.62006
[25] Sheridan, T.B., Reflections on information and information value, IEEE trans. syst. man cybern., 25, 1, 194-196, (1994)
[26] Sprent, P., Applied nnonparametric statistical methods, (1989), Chapman & Hall London
[27] Stevens, S.S., Scaling: A sourcebook for behavioral scientists, (1974), Aldine Chicago
[28] Tufte, E.R., Envisioning information, (1983), Graphic press Cheshire, Connecticut
[29] Tufte, E.R., The visual display of visual information, (1990), Graphic press Cheshire, Connecticut
[30] B. Walliser, Systèmes et modèles. Introduction critique à l’analyse des systèmes, Ed. Seuil, Paris, 1977 · Zbl 0364.93003
[31] Wickens, C.D.; Merwin, D.H.; Lin, E.L., Implications of graphics enhancements for the visualization of scientific data: dimensional integrality stereopsis, motion and mesh, Human fact., 36, 1, 44-61, (1994)
[32] Yager, R.A., Counting the number of classess in a fuzzy set, IEEE trans. syst. man cybern., 23, 1, 257-264, (1994)
[33] Yager, R.A.; Filev, D.P., Approximative clustering via the mountain method, IEEE trans. syst. man cybern., 24, 8, 1279-1284, (1994)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.