This paper describes a class of SQL injection attacks (SQLIA) where attackers can deduce information from the back-end database management system (DBMS) without transferring actual data. Instead, by using predetermined differentiation mechanism, information is being inferred piece by piece. Because of its widespread success, particularly in difficult situations where other SQLIA classes fail, u...

Inferential models (IMs) provide a general framework for prior-free, frequencycalibrated, posterior probabilistic inference. The fundamental idea is the use of unobservable auxiliary variables to describe the underlying uncertainty about the parameter of interest. When nuisance parameters are present, a marginalization step can reduce the dimension of the auxiliary variable, which in turn leads...

In various statistical model, such as density estimation and estimation of regression curves or hazard rates, monotonicity constraints can arise naturally. A frequently encountered problem in nonparametric statistics is to estimate a monotone density function f on a compact interval. A known estimator for density function of f under the restriction that f is decreasing, is Grenander estimator, ...

kernel density estimators are the basic tools for density estimation in non-parametric statistics.  the k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. in this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

This paper addresses inference in large panel data models the presence of both cross-sectional and temporal dependence unknown form. We are interested making inferences that do not rely on choice any smoothing parameter as is case with often employed “ H A C ” estimator for covariance matrix. To end, we propose a cluster asymptotic estimators valid bootstrap schemes require selection bandwidth ...

in statistical inference, the point estimation problem is very crucial and has a wide range of applications. when, we deal with some concepts such as random variables, the parameters of interest and estimates may be reported/observed as imprecise. therefore, the theory of fuzzy sets plays an important role in formulating such situations. in this paper, we rst recall the crisp uniformly minimum ...

We focus on the development of diagnostic tools and an R package called MNB for a multivariate negative binomial (MNB) regression model detecting atypical influential subjects. The is deduced from Poisson mixed in which random intercept follows generalized log-gamma (GLG) distribution. correlated count data leads to that inherits features hierarchical accommodate intraclass correlation occurren...

Understanding the effect of a particular treatment or policy pertains to many areas interest, ranging from political economics, marketing healthcare. In this paper, we develop non-parametric algorithm for detecting effects over time in context Synthetic Controls. The method builds on counterfactual predictions algorithms without necessarily assuming that correctly capture model. We introduce an...