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by Jeremy D Finn
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Multivariate analysis (MVA) is based on the statistical principle of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time. Typically, MVA is used to address the situations where multiple measurements are made on each experimental unit and the relations among these measurements and their structures are important. A modern, overlapping categorization of MVA includes: Normal and general multivariate models and distribution theory.
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Main Author: Finn, Jeremy D. Format: Book. International series in decision processes. The theory of linear models and multivariate analysis, by: Arnold, Steven . 1944- Published: (1981).
Main Author: Finn, Jeremy D. Subjects: Analysis of variance. An introduction to multivariate statistical analysis, by: Anderson, T. W. 1918- Published: (1984). Multivariate data analysis, by: Cooley, William W. Published: (1986). Multivariate analysis; proceedings. Introduction to bivariate and multivariate analysis, by: Lindeman, Richard Harold, 1926- Published: (1980).
The Clayton models, also called gamma frailty models, have been widely used for multivariate survival analysis. These models typically appear in either conditional or marginal formulations where covariates are incorporated through regression models
The Clayton models, also called gamma frailty models, have been widely used for multivariate survival analysis. These models typically appear in either conditional or marginal formulations where covariates are incorporated through regression models. The two formulations provide us the flexibility to delineate various types of dependence of survival times on covariates, along with the availability.
Series: Wiley Series in Probability and Statistics. Pages with related products. See and discover other items: multivariate analysis, multivariate statistics. Hardcover: 475 pages.