The problem of multicollinearity compromises the numerical stability of the regression coefficient estimate and cause some serious problem in validation and interpretation of the model. In this paper, we propose two new collinearity diagnostics for the detection of collinearity among regressors, based on coefficient of determination and adjusted coefficient of determination from auxiliary regression of regressors. A Monte Carlo simulation study has been conducted to compare the existing and proposed collinearity diagnostic tests. Comparison of diagnostics on some existing collinear data are also made
Multicollinearity has remained a major problem in regression analysis and should be sustainably addr...
Regression tends to give very unstable and unreliable regression weights when predictors are highly ...
Collinearity refers in a strict sense to the presence of exact linear relationships within a set of ...
The problem of multicollinearity compromises the numerical stability of the regression coefficient e...
Objectives. To demonstrate the ineffectiveness of some commonly used collinearity diagnostics, and p...
Multicollinearity is a statistical phenomenon in which predictor variables in a logistic regression ...
Investigators that seek to employ regression analysis usually encounter the problem of multicollinea...
In this paper, I explore the symptoms of multicollinearity, detection methods for multiple linear re...
Includes bibliographical references (leaves 118-120)The problems of detecting influential observatio...
Includes bibliographical references (leaves 118-120)The problems of detecting influential observatio...
Includes bibliographical references (leaves 118-120)The problems of detecting influential observatio...
Includes bibliographical references (leaves 118-120)The problems of detecting influential observatio...
Under the conditions of OLS use in order to perform multiple linear regressions, both the estimated ...
Multicollinearity in linear regression is typically thought of as a problem of large standard errors...
Multicollinearity has remained a major problem in regression analysis and should be sustainably addr...
Multicollinearity has remained a major problem in regression analysis and should be sustainably addr...
Regression tends to give very unstable and unreliable regression weights when predictors are highly ...
Collinearity refers in a strict sense to the presence of exact linear relationships within a set of ...
The problem of multicollinearity compromises the numerical stability of the regression coefficient e...
Objectives. To demonstrate the ineffectiveness of some commonly used collinearity diagnostics, and p...
Multicollinearity is a statistical phenomenon in which predictor variables in a logistic regression ...
Investigators that seek to employ regression analysis usually encounter the problem of multicollinea...
In this paper, I explore the symptoms of multicollinearity, detection methods for multiple linear re...
Includes bibliographical references (leaves 118-120)The problems of detecting influential observatio...
Includes bibliographical references (leaves 118-120)The problems of detecting influential observatio...
Includes bibliographical references (leaves 118-120)The problems of detecting influential observatio...
Includes bibliographical references (leaves 118-120)The problems of detecting influential observatio...
Under the conditions of OLS use in order to perform multiple linear regressions, both the estimated ...
Multicollinearity in linear regression is typically thought of as a problem of large standard errors...
Multicollinearity has remained a major problem in regression analysis and should be sustainably addr...
Multicollinearity has remained a major problem in regression analysis and should be sustainably addr...
Regression tends to give very unstable and unreliable regression weights when predictors are highly ...
Collinearity refers in a strict sense to the presence of exact linear relationships within a set of ...