In this paper we intend to improve the explanatory power of regressions when the deletion method is used for the remedy of Multicolinearity. If one deletes the variable (s) that is (are) responsible for Multicolinearity, he loses some information that is not common between the deleted variable (s) and the other remaining variables in the regression. To improve this method, we run the deleted va...

in this paper we intend to improve the explanatory power of regressions when the deletion method is used for the remedy of multicolinearity. if one deletes the variable (s) that is (are) responsible for multicolinearity, he loses some information that is not common between the deleted variable (s) and the other remaining variables in the regression. to improve this method, we run the deleted va...

In this paper we introduce two procedures for variable selection in cluster analysis and classification rules. One is mainly oriented to detect the “noisy” non–informative variables, while the other deals also with multicolinearity. A forward–backward algorithm is also proposed to make feasible these procedures in large data sets. A small simulation is performed and some real data examples are ...

The widely used method of least squares for state estimation is revisited. The commonly used least squares philosophy is based on the L 2 Hölder norm. The L 1 and L 8 norms are considered for applications in power engineering. The effects of outliers in measurements and multicolinearity on state estimation are studied. An application in parameter estimation for synchronous generators is given a...

Problems encountered in multiple regression due to multicolinearity or missing data can be overcome by using PLS regression. Several versions of the PLS regression algorithm exist. In this paper, we present a new version of this algorithm which can be extended to generalized linear regression models such as ordinal or multinomial logistic regression, generalized linear models, and Cox regressio...

Introduction: In Processes Modeling, when there is relatively a high correlation between covariates, multicollinearity is created, and it leads to reduction in model's efficiency. In this study, by using principle component analysis, modification of the effect of multicolinearity in Artificial Neural Network (ANN) and Logistic Regression (LR) has been studied. Also, the effect of multicolineari...

relationship between grain yield and its component traits can improve the efficiency of breeding programs by determining appropriate selection criteria. an investigation was carried out on barnyard millet (echinochloa spp.) global germplasm collection to investigate the association among yield components and their direct and indirect effects on the grain yield of barnyard millet. the experiment...

This study is performed to examine the effect of Current Ratio (CR), Debt To Equity (DER), Total Asset Turnover (TATO), Return On (ROE) and Earning per Share (EPS) toward stock price on Retail trade whole sale that listed Indonesian Stock Exchange from 2009 2012. Sampling technique used here purposive sampling obtain samples in accordance with predeterminan criteria. Method data analysis multip...