When Multicollinearity exists in a data set, the is considered deficient. frequently encountered observational studies. It creates difficulties when building regression models. phenomenon whereby two or more explanatory variable multiple model are highly correlated. Variable selection an important aspect of as such choice best subset among many variables to be included most difficult part analy...

An important question in designing cryptographic functions including substitution boxes S boxes is the relationships among the various nonlinearity criteria each of which indicates the strength or weakness of a cryptographic function against a particular type of cryptanalytic attacks In this paper we reveal for the rst time interesting connections among the strict avalanche characteristics di e...

The robustness of the results coming from an econometric application depends to a great extent on the quality of the sampling information. This statement is a general rule that becomes especially relevant in a spatial context where data usually have lots of irregularities. The purpose of this paper is to examine more closely this question paying attention to the impact of multicollinearity. It ...

Multicollinearity hampers empirical econometrics. The remedies proposed to date suffer from pitfalls of their own. The ridge estimator is not generally accepted as a vital alternative to the ordinary least−squares (OLS) estimator because it depends upon unknown parameters. The generalized maximum entropy estimator depends upon subjective exogenous information. This paper presents a novel maximu...

issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent articles by Cronbach (1987) and Dunlap and Kemery (1987) suggested the use of two transformations to reduce "problems" of multicollinearity. These transformations are discussed in the context of the conditional nature of multiple regression with ...

One of the most problematic issues in contemporary meta-analysis is the estimation and interpretation of moderating effects. Monte Carlo analyses are developed in this article that compare bivariate correlations, ordinary least squares and weighted least squares (WLS) multiple regression, and hierarchical subgroup (HS) analysis for assessing the influence of continuous moderators under conditio...