abstract. a practical common weight scalarizing function methodology with an improved discriminating power for technology selection is introduced. the proposed scalarizing function methodology enables the evaluation of the relative efficiency of decision-making units (dmus) with respect to multiple outputs and a single exact input with common weights. its robustness and discriminating power are...

Reduced rank regression extracts an essential information from examples of input-output pairs. It is understood as a three-layer neural network with linear hidden units. However, reduced rank approximation is a non-regular statistical model which has a degenerate Fisher information matrix. Its generalization error had been left unknown even in statistics. In this paper, we give the exact asympt...

his work proposes a blind adaptive reduced-rank scheme and constrained constant-modulus (CCM) adaptive algorithms for interference suppression in wireless communications systems. The proposed scheme and algorithms are based on a two-stage processing framework that consists of a transformation matrix that performs dimensionality reduction followed by a reduced-rank estimator. The complex structu...

In a recent paper by K.-H. Lee and K. Lee, rigid reflections are defined for any Coxeter group via non-self-intersecting curves on Riemann surface with labeled curves. When the arises from an acyclic quiver, related to representations of quiver. For family rank 3 groups, it was conjectured in same that there is natural bijection set reduced positive roots symmetric 2 Kac–Moody algebra onto corr...

Likelihood ratio criteria are developed for hypotheses concerning multivariate regression matrices when reduced rank is assumed or equivalently hypotheses concerning linear restrictions on regression matrices. In an econometric simultaneous equation model a single restriction may be called a ”structural equation.” A model of reduced rank in statistics is often termed a ”linear functional relati...