DEA (Data Envelopment Analysis) is a technique for evaluating the relative effectiveness of decision-making units (DMU) with multiple inputs and outputs data based on non-parametric modeling using mathematical programming (including linear programming, multi-parameter programming, stochastic programming, etc.). The classical DEA methods are developed to handle the information in the form of cri...

in this study, farm level technical efficiency (te) and its determinants were investigated. a stochastic cobb-douglas (cd) production frontier was used to provide estimates of input-oriented te for a sample of 100 rain fed farms in two palestinian governorates. empirical findings showed that the estimated te of the farms in the pooled sample ranged from 35.7 to 95.6%, with a mean value of 72.2%...

background determinants of health or health production function in health economics literature constitute noticeable issues in health promotion. this study aimed at estimating a health production function for east mediterranean region (emr) based on the grossman theoretical model.   methods this ecological study was performed using the econometric methods. the panel data model was used in order...

nowadays, hidden markov models are extensively utilized for modeling stochastic processes. these models help researchers establish and implement the desired theoretical foundations using markov algorithms such as forward one. however, using stability hypothesis and the mean statistic for determining the values of markov functions on unstable statistical data set has led to a significant reducti...

In this paper, we present a recurrent neural network model for solving CCR Model in Data Envelopment Analysis (DEA). The proposed neural network model is derived from an unconstrained minimization problem. In the theoretical aspect, it is shown that the proposed neural network is stable in the sense of Lyapunov and globally convergent to the optimal solution of CCR model. The proposed model has...

We show that deep ReLU neural network classifiers can see a low-dimensional Riemannian manifold structure on data. Such comes via the \sl local data matrix, variation of Fisher information where role model parameters is taken by variables. obtain foliation domain, and we dataset which trained lies leaf, whose dimension bounded number classification labels. validate our results with some experim...