Breed additive, dominance, and epistatic loss effects are of concern in the genetic evaluation of a multibreed population. Multiple regression equations used for fitting these effects may show a high degree of multicollinearity among predictor variables. Typically, when strong linear relationships exist, the regression coefficients have large SE and are sensitive to changes in the data file and...

the objective followed in the present study was to survey the relationship between 18 body trait measurements (live weight, height at wither, paunch girth, neck diameter, body length, girth around the body, width of fat tail at above, below and midpoint of fat tail, fat tail length lowers right and left sides, fat tail gap length, fat tail depth at the above, below, and midpoint, and girth arou...

In this paper we deal with comparisons among several estimators available in situations of multicollinearity (e.g., the r k class estimator proposed by Baye and Parker, the ordinary ridge regression (ORR) estimator, the principal components regression (PCR) estimator and also the ordinary least squares (OLS) estimator) for a misspecified linear model where misspecification is due to omission of...

Fuzzy regression models has been traditionally considered as a problem of linear programming. The use of quadratic programming allows to overcome the limitations of linear programming as well as to obtain highly adaptable regression approaches. However, we verify the existence of multicollinearity in fuzzy regression and we propose a model based on Ridge regression in order to address this prob...

Background: Two main issues that challenge model building are number of Events Per Variable and multicollinearity among exploratory variables. Our aim is to review statistical methods that tackle these issues with emphasize on penalized Lasso regression model.  The present study aimed to explain problems of traditional regressions due to small sample size and m...

Multicollinearity in factor analysis has negative effects, including unreliable structure, inconsistent loadings, inflated standard errors, reduced discriminant validity, and difficulties interpreting factors. It also leads to stability, hindered replication, misinterpretation of importance, increased parameter estimation instability, power detect the true compromised model fit indices, biased ...