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There is a need for tractable models for multivariate data with nonstandard dependence structures.
Logistic regression is the most common statistical model for processing multivariate data in the medical literature.
There are several statistical models applicable for multivariate correlation scoring (instead of two-class based scoring).
Zhang, Y., Zhou, H., Zhou, J. & Sun, W. Regression models for multivariate count data.
Thus, most papers published on this issue are based on statistical techniques such as multivariate data analysis [10] or mathematical models of dependence [11-13] [11-13]
Statistical experimental design and multivariate data analysis provided statistically significant regression models which subsequently were used to establish exposure response relationship for photosynthetic efficiency as function of sediment coverage.
Copula-based models are a general set of statistical models defined for any multivariate random variable.
I took courses such as Statistical Models, Statistical Consulting, Multivariate Analysis, Time Series Analysis, Statistical Genetics, Estimation Theory.
SPSS 16.0 version statistical program was used for descriptive statistics and multivariate data analysis.
Univariate, bivariate and multivariate statistical models were used for the evaluation of data.
For this, we developed multivariate spatial statistical models on the basis of mountain-wide land-use change data, evaluated between the 1980s and 1990s, and selected geo-physical and socio-economic variables.
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