Multicollinearity Robust QAP for Multiple-Regression
نویسندگان
چکیده
The quadratic assignment procedures for inference on multiple-regression coefficients(MRQAP) has become popular in social network analysis. These tests have been developed to assess the sizes of a set of multiple-regression coefficients. However, research practitioners often use these tests to assess the size of individual multiple-regression coefficients. Although this might be a harmless extension, our our concern focuses on this practice under conditions of multicollinearity. In this paper we show analytically that different MRQAP-tests for individual parameter estimates are biased under multicollinearity. Subsequently, we propose a new MRQAP-test, which we call ”semi-partialing” that is robust against multicollinearity. Extensive simulation results, as well as re-analysis of the classic Laumann-Marsden-Galaskiewicz(1978)data show the added value of this new ”semi-partialing” method over the existing methods.
منابع مشابه
Robust Estimation in Linear Regression with Molticollinearity and Sparse Models
One of the factors affecting the statistical analysis of the data is the presence of outliers. The methods which are not affected by the outliers are called robust methods. Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers. Besides outliers, the linear dependency of regressor variables, which is called multicollinearity...
متن کاملWeighted Ridge MM-Estimator in Robust Ridge Regression with Multicollinearity
This study is about the development of a robust ridge regression estimator. It is based on weighted ridge MM-estimator (WRMM) and is believed to have potentials in remedying the problems of multicollinearity. The proposed method has been compared with several existing estimators, namely ordinary least squares (OLS), robust regression based on MM estimator, ridge regression (RIDGE), weighted rid...
متن کاملMulticollinearity: Causes, Effects And
If there is no linear relationship between the regressors, they are said to be orthogonal. Multicollinearity is a case of multiple regression in which the predictor variables are themselves highly correlated. If the goal is to understand how the various X variables impact Y, then multicollinearity is a big problem. Multicollinearity is a matter of degree, not a matter of presence or absence. In...
متن کاملComments on Statistical Issues in January 2015
In this section, we address the problem of multicollinearity in multiple regression analysisthat appeared in the article titled, " Correlation between frailty and cognitive function in non-demented community dwelling older Koreans, " published in November 2014 by Kim et al. This is one of the most frequent comments made about articles usingmultiple regression analysis. Multicollinearity indicat...
متن کاملConfronting Multicollinearity in Ecological Multiple Regression
The natural complexity of ecological communities regularly lures ecologists to collect elaborate data sets in which confounding factors are often present. Although multiple regression is commonly used in such cases to test the individual effects of many explanatory variables on a continuous response, the inherent collinearity (multicollinearity) of confounded explanatory variables encumbers ana...
متن کامل