Modeling with Gaussian mixture regression for lactationmilk yield in Anatolian buffaloes

Abstract

The purpose of this study was to classify Anatolian buffalo using Gaussian mixture regression model according to discrete and continuous environmental effects. Gaussian mixture model performs separately regression analysis both within and between groups. This is an important property of Gaussian mixture models which makes it different from other multivariate statistical methods. The data were obtained from 1455 Anatolian buffalo lactation milk yield records reared in seven different locations in Bitlis province, Turkey. Age of dam, lactation duration and locations were considered as environmental effects on lactation milk yield. Data set was divided into three homogenous subgroups with respect to AIC and BIC in the Gaussian mixture regression, based on environmental effects on lactation milk yield. Estimated mean for lactation milk yields and mixing probabilities for the first, second and third subgroups were determined as 1494.33 kg (16.9%), 540.33 kg (45.2%) and 847.61 (37.9%), respectively. The numbers of buffalo in each subgroup according to mixing probability were obtained as 159, 756, and 540 for the first, second, and third groups, respectively. The effects of lactation period, age of dam and villages were found statistically significant on lactation milk yield in subgroup 1 that was highest mean for lactation milk yield (p<0.01). In conclusion, results showed that Gaussian mixture regression was an important tool for classifying quantitative traits considering environmental effects in animal breeding.

CHANGES IN ION (K, Ca AND Na) REGULATION, ANTIOXIDANT ENZYME ACTIVITY AND PHOTOSYNTHETIC PIGMENT CONTENT IN MELON GENOTYPES SUBJECTED TO SALT STRESS – A MIXTURE MODELING ANALYSIS

“…The need for new models to evaluate experimental data sets is being increasingly recognized, with many disciplines turning to a Gaussian-mixture model for analysis of normally distributed data [Yeşilova et al 2010[Yeşilova et al , 2016. Mixture modeling aims to identify previously unobserved homogenous sub-populations comprising a seemingly heterogenous data set [Wang et al 1996, Dalrymple et al 2003, Martinez et al 2009] using Akaike's data criteria (AIC) and Bayesian data criteria (BIC) [Yeşilova et al 2010] to define and separate sub-populations.…”