The Prediction of Live Weight of Hair Goats Through Penalized Regression Methods: Lasso and Adaptive Lasso

Abstract

The least absolute selection and shrinkage operator (LASSO) and adaptive LASSO methods have become a popular model in the last decade, especially for data with a multicollinearity problem. This study was conducted to estimate the live weight (LW) of Hair goats from biometric measurements and to select variables in order to reduce the model complexity by using penalized regression methods: LASSO and adaptive LASSO for gamma = 0.5 and gamma = 1. The data were obtained from 132 adult goats in Honaz district of Denizli province. Age, gender, forehead width, ear length, head length, chest width, rump height, withers height, back height, chest depth, chest girth, and body length were used as explanatory variables. The adjusted coefficient of determination (R-adj(2)), root mean square error (RMSE), Akaike's information criterion (AIC), Schwarz Bayesian criterion (SBC), and average square error (ASE) were used in order to compare the effectiveness of the methods. It was concluded that adaptive LASSO (gamma = 1) estimated the LW with the highest accuracy for both male (R-adj(2 )= 0.9048; RMSE = 3.6250; AIC = 79.2974; SBC = 65.2633; ASE = 7.8843) and female (R-adj(2 ) = 0.7668; RMSE = 4.4069; AIC = 392.5405; SBC = 308.9888; ASE = 18.2193) Hair goats when all the criteria were considered.