Data envelopment analysis which is a nonparametric technique for evaluating relative efficiency of the decision making units with multiple inputs and outputs, has been a very popular method among researchers. While this nonparametric technique is popular, it has some drawbacks such as lack of discrimination in efficient units and weights dispersion .The present study, which is a model based on ...

The plausibility of competing statistical models may be assessed using penalized log-likelihood criteria such as the AIC, which is given by AIC = −2lnL + 2k (L being the maximum likelihood estimate and k the number of free parameters). The raw AIC values can be transformed to AIC model weights by wi = exp(− 2∆AICi)/ ∑R r=1 exp(− 2∆AICr), where ∆AICi = AICi − min(AIC) and R is the total number o...

Risk bounds are derived for regression estimation based on model selection over an unrestricted number of models. While a large list of models provides more flexibility, significant selection bias may occur with model selection criteria like AIC. We incorporate a model complexity penalty term in AIC to handle selection bias. Resulting estimators are shown to achieve a trade-off among approximat...

−Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction ...

Typically, in the practice of causal inference from observational studies, a parametric model is assumed for the joint population density of potential outcomes and treatment assignments, and possibly this is accompanied by the assumption of no hidden bias. However, both assumptions are questionable for real data, the accuracy of causal inference is compromised when the data violates either assu...

Standard goodness-of-fit tests for a parametric regression model against a series of nonparametric alternatives are based on residuals arising from a fitted model.When a parametric regression model is compared with a nonparametric model, goodness-of-fit testing can be naturally approached by evaluating the likelihood of the parametric model within a nonparametric framework. We employ the empiri...

Abstract Cancer radiomics is an emerging discipline promising to elucidate lesion phenotypes and tumour heterogeneity through patterns of enhancement, texture, morphology shape. The prevailing technique for image texture analysis relies on the construction synthesis grey-level co-occurrence matrices (GLCM). Practice currently reduces structured count data a GLCM reductive redundant summary stat...