data envelopment analysis (dea) models with interval inputs and outputs have been rarely discussed in dea literature. this paper, using the enhanced russell measurement proposes an extended model which permits the presence of interval scale variables which can take both negative and positive values. the model is compared with most well-known dea models of which include the ccr model, the bcc mo...

in recent decades, the concept of cultural capital has created a lot of scientific studies over the world in the sociology of education. these studies mainly based on the two models: bourdieu's cultural reproduction and dimaggio's cultural mobility. these have achieved different results. this study aimed at evaluating the theoretical models in an empirical condition. middle and high school stud...

Value at Risk (VaR) is a widely used measure of market risk. Precision in the estimation volatility leads to accurate VaR forecasts. As time-varying and has clustering effect, GARCH class models helpful modeling more precisely. Studies have also shown that range-based estimates are efficient than traditional use only closing prices. Therefore, this study uses family model forecast VaR. The comp...

The pioneering works in Agent-Based Modeling (ABM) notably Schelling (1969) and Epstein and Axtell (1996) introduced the method for testing hypotheses in "complex thought experiments" (Cederman 1997, 55). Although purely theoretical experiments can be important, the empirical orientation of the social sciences demands that the gap between modeled "thought experiments" and empirical data be as n...

This paper proposes an asymptotically efficient method for estimating models with conditional moment restrictions. Our estimator generalizes the maximum empirical likelihood estimator (MELE) of Qin and Lawless (1994). Using a kernel smoothing method, we efficiently incorporate the information implied by the conditional moment restrictions into our empirical likelihood-based procedure. This yiel...

Achieving peak performance from the computational kernels that dominate application performance often requires extensive machine-dependent tuning by hand. Automatic tuning systems have emerged in response, and they typically operate by (1) generating a large number of possible, reasonable implementations of a kernel, and (2) selecting the fastest implementation by a combination of heuristic mod...