Results 201 to 210 of about 997,763 (227)
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The discarding of variables in multivariate analysis
Biometrika, 1967In many multivariate situations we are presented with more variables than we would like, and the question arises whether they are all necessary and if not which can be discarded. In this paper we consider two such situations. (a) Regression analysis. The problem here is whether any variables can be discarded as adding little or nothing to the accuracy ...
E M, Beale, M G, Kendall, D W, Mann
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Multivariate Behavioral Research, 1981
Analysis of multivariate aptitude-treatment-interaction data is discussed. Various global hypotheses and the associated follow-up descriptions are considered and compared. Analysis procedures are illustrated using data from a science education study to describe the interaction between student reading ability and the time allowed in an individualized ...
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Analysis of multivariate aptitude-treatment-interaction data is discussed. Various global hypotheses and the associated follow-up descriptions are considered and compared. Analysis procedures are illustrated using data from a science education study to describe the interaction between student reading ability and the time allowed in an individualized ...
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Multivariate Statistical Analysis
Psychophysiology, 1973ABSTRACTGeneral multivariate statistical theory and three statistical models (multi‐variate analysis of variance, canonical correlation analysis, and factor analysis) are described. Numerical examples, mathematical notes, an annotated bibliography, and references to computer programs are presented.
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Multivariate analysis using ‘and’ and ‘or’
Mathematical Social Sciences, 1984Extensions from binary to continuous variables are considered of mathematical operators for connectives (''and'', ''or'', ''exclusive or'', etc). Using concepts from fuzzy set theory it is illustrated how such extensions can be used in statistical modelling of interactions in multivariate analysis.
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An Inequality with Application to Multivariate Analysis
Biometrika, 1975An inequality for the trace of the product of two symmetric matrices can be used to simplify the proofs of a number of results in multivariate analysis. This is illustrated on two problems: estimating the parameters of a multivariate normal population, and estimating a system of linear functional relationships between the mean vectors of k multivariate
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Multivariate Data and Multivariate Analysis
2005Multivariate data arise when researchers record the values of several random variables on a number of subjects or objects or perhaps one of a variety of other things (we will use the general term “units”) in which they are interested, leading to a vector-valued or multidimensional observation for each.
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Multivariate Analysis in Genetic Studies
Acta geneticae medicae et gemellologiae, 1972SummaryThe results are presented of a method of generalized distances calculated by a noncentral χ2 test and applied to compare 63 twin pairs and 196 sib pairs. The advantage of this method in biometrical analysis lies in the fact that several measurements can be utilised simultaneously.
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Multivariate Survival Analysis
Theory of Probability & Its Applications, 1993The author considers problems arising in multivariate survival analysis. It is shown that concepts of history and future of one-dimensional time lead to definitions of multidimensional measures of risk, iterated measures of hazard rate, and a representation of multidimensional survival time.
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On „Multivariate Logit Analysis”︁
Biometrische Zeitschrift, 1975Kullback, S., Fisher, M.
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2006
This new edition has been fully revised to build on the enormous success of its popular predecessor. It now includes new features introduced by readers' requests including a new chapter on propensity score, more detail on clustered data and Poisson regression and a new section on analysis of variance.
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This new edition has been fully revised to build on the enormous success of its popular predecessor. It now includes new features introduced by readers' requests including a new chapter on propensity score, more detail on clustered data and Poisson regression and a new section on analysis of variance.
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