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On the calculation of derived variables in the analysis of multivariate responses

1992

AbstractThe multivariate regression of a p × 1 vector Y of random variables on a q × 1 vector X of explanatory variables is considered. It is assumed that linear transformations of the components of Y can be the basis for useful interpretation whereas the components of X have strong individual identity. When p ≥ q a transformation is found to a new q × 1 vector of responses Y∗ such that in the multiple regression of, say, Y1∗ on X, only the coefficient of X1 is nonzero, i.e. such that Y1∗ is conditionally independent of X2, …, Xq, given X1. Some associated inferential procedures are sketched. An illustrative example is described in which the resulting transformation has aided interpretation.

canonical analysisStatistics and ProbabilityMultivariate statisticsPure mathematicsNumerical AnalysisMultivariate analysisBasis (linear algebra)conditional independencederived variableCanonical analysisCombinatoricsgraphical chain modelTransformation (function)multivariate linear modelConditional independenceLinear regressionStatistics Probability and UncertaintyRandom variableMathematicsJournal of Multivariate Analysis
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