Abstract
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 γ=0.5 and γ=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 (Radj2), 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 (γ=1) estimated the LW
with the highest accuracy for both male (Radj2=0.9048; RMSE = 3.6250; AIC = 79.2974; SBC = 65.2633; ASE = 7.8843)
and female (Radj2=0.7668; RMSE = 4.4069; AIC = 392.5405; SBC = 308.9888; ASE = 18.2193) Hair goats when all the criteria were considered.
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