during the last years, the concept of input congestion and technical ineff-ciency in data envelopment analysis (dea), have been investigated by manyresearchers. the motivation of this paper is to present models which obtain thedecreased output value due to input congestion and technical ineciency. more-over, we extend the models to estimate input congestion, technical ineciencyand output red...

We introduce a technique for identification of systems with arbitrarily time-variant responses from samples of their input and output signals, and without using any prior information about the dynamics of the unknown system response. Our technique is based on the use of optimized averaging filters for the estimation of time-variant second order moments. We demonstrate the utility of our approac...

In a recent paper by Vencheh and Esmaeilzadeh, a super-efficiency model based on RDM model is formulated where some input and output data are negative. However, we show that the formulation in the paper is not always feasible or bounded for its purpose and cannot produce the numerical results claimed. Moreover, we demonstrate that the super-efficiency model based on RDM model is unable to provi...

We present a novel counting network construction, where the number of input wires w is smaller than or equal to the number of output wires t. The depth of our network is Θ(lg w), which depends only on w. In contrast, the amortized contention of the network depends on the number of concurrent processes n and the parameters w and t. This offers more flexibility than all previously known networks,...

The output data from a structure is the building block for output-only modal analysis. The structure response in the output data, however, is usually contaminated with noise. Naturally, the success of output-only methods in determining the modal parameters of a structure depends on noise level. In this paper, the possibility and accuracy of identifying the modal parameters of a simply supported...

We derive a modified version of factor analysis for data that is poisson (rather than gaussian) distributed. This modified approach may better fit certain classes of data, including neuronal spiking data commonly collected in electrophysiology experiments.