The purpose of this study is to provide an efficient method for the selection input–output indicators in data envelopment analysis (DEA) approach, order improve discriminatory power DEA evaluation process and performance homogeneous decision-making units (DMUs) presence negative values data. For purpose, Shannon entropy technique used as one most important methods determining weight indicators....

In this paper we consider synchronous parallel programs P that are composed by sequential randomized processes S Sk which communicate via shared variables First we give an operational semantics for the sequential components Si on the basis of a transition relation de ned in the classical SOS style a la Plotkin Plo which we use to specify the behaviour of P by a Markov chain whose transitions st...

In this article, we investigate the consistency of parameter estimates obtained from least-squares identification with a quadratic parameter constraint. For generality, we consider infinite impulse-response systems with coloured input and output noise. In the case of finite data, we show that there always exists a possibly indefinite quadratic constraint depending on the noise realisation that ...

In this paper, the problem of identifying stochastic linear discrete-time systems from noisy input/output data is addressed. The input noise is supposed to be white, while the output noise is assumed to be coloured. Some methods based on instrumental variable techniques are studied and compared to a least squares bias compensation scheme with the help of Monte Carlo simulations.

Many theoretical constructs of interest to psychologists are multidimensional and derive from the integration of several input variables. We show that input variables that are measured on ordinal scales cannot be combined to produce a stable weakly ordered output variable that allows trading off the input variables. Instead a partial order is obtained in which the amount of ordering depends on ...

In this paper, the problem of identifying stochastic linear discrete-time systems from noisy input/output data is addressed. The input noise is supposed to be white, while the output noise is assumed to be coloured. Some methods based on instrumental variable techniques are studied and compared to a least squares bias compensation scheme with the help of Monte Carlo simulations.