Non-parametric DEA is a technique on the basic of mathematical programming to determine the efficiency of homological decision making units. DEA models changes in demand cause changes in variations in output levels and also will cause changes in a firm’s inefficiency. Often a firm can adjust input influencing on the output level. Models designed with technique on DEA that considers changes in d...

The aim of statistical modeling is to identify the model that most closely approximates the underlying process. Akaike information criterion (AIC) is commonly used for model selection but the precise value of AIC has no direct interpretation. In this paper we use a normalization of a difference of Akaike criteria in comparing between the two rival models under unified hybrid cens...

We introduce -proper interval graphs as interval graphs with interval models in which no interval is properly contained in more than other intervals, and also provide a forbidden induced subgraph characterization of this class of graphs. We initiate a graph-theoretic study of subgraphs of -proper interval graphs with maximum clique size and give an equivalent characterization of these graphs by...

This paper addresses the problem of constructing reliable interval predictors directly from observed data. Differently from standard predictor models, interval predictors return a prediction interval as opposed to a single prediction value. We show that, in a stationary and independent observations framework, the reliability of themodel (that is, the probability that the future systemoutput fal...

The most general one dimensional reaction-diffusion model with nearestneighbor interactions solvable through the empty interval method, and without any restriction on the particle-generation from two adjacent empty sites is studied. It is shown that turning on the reactions which generate particles from two adjacent empty sites, results in a gap in the spectrum of the evolution operator (or equ...

We examine the use of hidden Markov and hidden semi-Markov models for automatically segmenting an electrocardiogram waveform into its constituent waveform features. An undecimated wavelet transform is used to generate an overcomplete representation of the signal that is more appropriate for subsequent modelling. We show that the state durations implicit in a standard hidden Markov model are ill...

This article presents a new method for generating discrete implicit surface models using interval arithmetics. This approach is unique to our knowledge and has the advantage of profiting of voxel representation for both modelling and visualization purposes. We’ve adopted high discrete resolutions to obtain better surface represention and realistic visualisation. Interval arithmetics guarantees ...

Having a regression model, we are interested in finding two-sided intervals that are guaranteed to contain at least a desired proportion of the conditional distribution of the response variable given a specific combination of predictors. We name such intervals predictive intervals. This work presents a new method to find two-sided predictive intervals for non-parametric least squares regression...

Most of the computations and tasks performed by the brain require the ability to tell time, and process and generate temporal patterns. Thus, there is a diverse set of neural mechanisms in place to allow the brain to tell time across a wide range of scales: from interaural delays on the order of microseconds to circadian rhythms and beyond. Temporal processing is most sophisticated on the scale...

For a proper assessment of risks associated with the trading of derivatives, the performance of hedging strategies should be evaluated not only in the context of the idealized model that has served as the basis of strategy development, but also in the context of other models. In this paper we consider the class of so-called interval models as a possible testing ground. In the context of such mo...