In assessing the relative efficiency of decision-maker units by classical Data Envelopment Analysis (DEA) models, the status of the data is determined from the input or output points of views. In real issues, there are some data whose statuses are debatable. Some decision making units consider them as input to achieve higher efficiency while some other decision making units consider them ...

data envelopment analysis (dea) has been proven as an efficient technique to evaluate the performance of homogeneous decision making units (dmus) where multiple inputs and outputs exist. in the conventional applications of dea, the data are considered as specific numerical values with explicit designation of being an input or output. however, the observed values of the data are sometimes imprec...

Abstract Instrumental variables are often associated with low estimator precision. This paper explores efficiency gains which might be achievable using moment conditions which are nonlinear in the disturbances and are based on flexible parametric families for error distributions. We show that these estimators can achieve the semiparametric efficiency bound when the true error distribution is a ...

background: identification of disease risk factors can help in the prevention of diseases. in assessing the predictive value of continuous variables, a routine procedure is to categorize the factors. this yield to inability to detect non‑linear relationship, if exist. multivariate fractional polynomial (mfp) modeling is a flexible method to reveal non‑linear associations. we aim to demonstrate ...

In conventional DEA, measures are classified as either input or output. However, in some real cases there are variables whose status is not known as input or output before assessment. These variable are known as flexible measures. One of the most important economic dimensions for ensuring the success of a company is the efficiency with which it uses its resources. Therefore, centering on Asses...

a confounder is a variable whose presence affects the variables being studied so that the results do not reflect the actual relationship. there are various ways to exclude or control confounding variables including randomization, restriction and matching. but all these methods are applicable at the time of study design. when experimental designs are premature, impractical, or impossible, resear...