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Multivariate Analysis of Incomplete Mapped Data
Transactions in GIS, 2003AbstractClassical multivariate analyses are based on matrix algebra and enable the analysis of a table containing measurements of a set of variables for a set of sites. Incomplete mapped data consist of measurements of a set of variables recorded for the same geographical region but for different zonal systems and with only a partial sampling of this ...
Dray, S., Pettorelli, N., Chessel, D.
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Multivariate Entropy Analysis of Network Data
Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 2015Multigraphs with numerical or qualitative attributes defined on vertices and edges can benefit from systematic methods based on multivariate entropies for describing and analysing the interdependencies that are present between vertex and edge attributes. This is here illustrated by application of these tools to a subset of data on the social relations
Ove Frank, Termeh Shafie
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Multivariate data analysis of multivariate populations
Chemometrics and Intelligent Laboratory Systems, 2007For data that can be arranged in populations, multivariate data analysis of digitalized distributions is suggested as an alternative method to detect variations. This approach includes a representation of the data that avoids information destroying pre-processing such as averaging.
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Applied Multivariate Data Analysis
The American Statistician, 2002(2002). Applied Multivariate Data Analysis. The American Statistician: Vol. 56, No. 3, pp. 248-249.
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Different chapters within this book and the literature prove the ToF-SIMS as a powerful surface analysis tool. However, one large drawback is that data analysis can be challenging and is very time-consuming compared to the measurement time and to other analysis techniques. Often only a small subset of data is analysed, e.g.
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1975
Abstract : This paper contains an account of several techniques in multivariate data analysis. Included among these techniques are classification and clustering procedures, multidimensional contingency table analysis, and some graphical representation techniques. Some data bases are employed to illustrate the techniques.
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Abstract : This paper contains an account of several techniques in multivariate data analysis. Included among these techniques are classification and clustering procedures, multidimensional contingency table analysis, and some graphical representation techniques. Some data bases are employed to illustrate the techniques.
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Multidimensional Fitting for Multivariate Data Analysis
Journal of Computational Biology, 2010Large multidimensional data matrices are frequent in biology. However, statistical methods often have difficulties dealing with such matrices because they contain very complex data sets. Consequently variable selection and dimensionality reduction methods are often used to reduce matrix complexity, although at the expense of information conservation. A
Claude, Berge +6 more
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A MULTIVARIATE ANALYSIS OF FAMILY DATA
American Journal of Epidemiology, 1981The authors describe the application of multivariate analysis to the problem of estimating intra-family correlations and testing them for statistical significance. This is illustrated by a re-analysis of survey data collected by Miall and Oldham (Clin Sci 1958;17:459-87) on the familial aggregation of blood pressure.
A, Donner, J J, Koval
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Multivariate Data Analysis (Chemometrics)
2014Chemometrics plays a key role in PAT strategies. It is essential in understanding and diagnosing real-time processes, and keeping them under multivariate statistical control. This chapter will cover design of experiments, exploratory analysis, quantitative predictive modelling, classification, multivariate process monitoring and multi-block and multi ...
Sylvie Roussel +3 more
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