Multicollinearity is a topic that is often discussed accordingly with linear regression analysis. One of the assumptions for a multiple linear regression model is that the predictor variables, which make up the X matrix, are assumed to be uncorrelated. This assumption ensures the errors from the ordinary least squares estimators will be uncorrelated. However, true independence amongst the predi...

The current trend in genome-wide association studies is to identify regions where the true disease-causing genes may lie by evaluating thousands of single-nucleotide polymorphisms (SNPs) across the whole genome. However, many challenges exist in detecting disease-causing genes among the thousands of SNPs. Examples include multicollinearity and multiple testing issues, especially when a large nu...

Although fuzzy regression is widely employed to solve many problems in practice, what seems to be lacking is the problem of multicollinearity. In this paper, the fuzzy centers principal component analysis is proposed to first derive the fuzzy principal component scores. Then the fuzzy principal component regression (FPCR) is formed to overcome the problem of multicollinearity in the fuzzy regre...

Multicollinearities among the coefficients obtained from the method of geographically weighted regression have been identified in recent research. This is a serious issue that poses a critical challenge for the utility of the method as a tool to investigate multivariate relationships. The evidence regarding the ability of GWR to retrieve spatially varying processes remains mixed due to partial ...

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...

Algorithmic trading is a common topic researched in the neural network due to abundance of data available. It phenomenon where an approximately linear relationship exists between two or more independent variables. especially prevalent financial interrelated nature data. The existing feature selection methods are not efficient enough solving such problem potential loss essential and relevant inf...

Multicollinearity negatively affects the efficiency of maximum likelihood estimator (MLE) in both linear and generalized models. The Kibria Lukman (KLE) was developed as an alternative to MLE handle multicollinearity for regression model. In this study, we proposed Logistic Kibria-Lukman (LKLE) logistic We theoretically established superiority condition new over MLE, ridge (LRE), Liu (LLE), Liu...

This paper presents a comprehensive analysis of multicollinearity problem in data fitting. Data fitting is stated as a single-objective optimization problem where an objective function indicates the error of approximation the target vector with a some function of given features. The linear dependence between features means that the multicollinerity problem exists and leads to unstability and re...

The Human Development Index (HDI) is an important indicator in measuring the success of national development. Central Java with a high population can be considered as obstacle and driver To find out factors that affect HDI, it necessary to make model. One statistical methods used multiple linear regression analysis. However, modeling there are assumptions must met, namely linearity, normality, ...