Thesis (Ph.D.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Statistics and Econometrics

Many methods had been recommended to explain the ambiguous concept of relative importance for independent variables in multiple regression models. One of the most important approaches for determining the relative importance of predictors is dominance analysis, which is a technique that determines variable importance, based on comparisons of unique variance contributions of all pairs of variables to regression equations involving all possible subsets of predictors.Relative importance analysis (e.g Dominance Analysis) is a very useful supplement to regression analysis. The purpose of determining predictor importance is not model selection but rather uncovering the individual contributions of predictors relative to each other within a selected model.Dominance analysis offers a general framework for the determination of relative importance of predictors in univariate and multivariate multiple regression models.This approach relies on pairwise comparisons of the contribution of predictors in all relevant subset models.One of the great weaknesses of Dominance Analysis (DA) which is, it turns into extra computationally hard because of the exponentially growing number of sub-models involved.That is due to the fact for predictors, there are 2^P {u2013} 1submodel. As an instance, with 3 predictors a Dominance Analysis summary table will have seven possible models (i.e. (1) X₁ (2) X₂, (3) X₃, (4) X₁and X₂, (5) X₂and X₃, (6) X₁ and X₃, (7) X₁, X₂, and X₃). Likewise, 10 predictors will yield 1023 possible sub-models

Issued also as CD