Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1981 · 115 citations · 11 references
Reduced-rank RegressionCovariance MatrixEngineeringHigh-dimensional MethodCanonical AnalysisReduced-rank Regression ModelStatistical InferenceDimensionality ReductionEstimation TheoryMultivariate AnalysisStatisticsLow-rank Approximation
SUMMARY We show that maximum-likelihood analysis of the reduced-rank regression model exploits a fundamental inequality result of matrix theory when a normally distributed error structure with unknown covariance is assumed. This approach closely parallels the corresponding analysis when the covariance matrix is known (Davies and Tso, 1980) and demonstrates straightforwardly the intimate connection between reduced-rank regression and canonical analysis. A geometric interpretation of the analysis is given.
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