Naeemipour Younesi Hossein, Shariati Mohammad Mahdi, Zerehdaran Saeed, Jabbari Nooghabi Mehdi, Løvendahl Peter
Department of Animal Science,Ferdowsi University of Mashhad,91779 Mashhad,Iran.
Department of Statistics,Ferdowsi University of Mashhad,91779 Mashhad,Iran.
J Dairy Res. 2019 Feb;86(1):19-24. doi: 10.1017/S0022029919000013. Epub 2019 Feb 7.
The main objective of this study was to compare the performance of different 'nonlinear quantile regression' models evaluated at the τth quantile (0·25, 0·50, and 0·75) of milk production traits and somatic cell score (SCS) in Iranian Holstein dairy cows. Data were collected by the Animal Breeding Center of Iran from 1991 to 2011, comprising 101 051 monthly milk production traits and SCS records of 13 977 cows in 183 herds. Incomplete gamma (Wood), exponential (Wilmink), Dijkstra and polynomial (Ali & Schaeffer) functions were implemented in the quantile regression. Residual mean square, Akaike information criterion and log-likelihood from different models and quantiles indicated that in the same quantile, the best models were Wilmink for milk yield, Dijkstra for fat percentage and Ali & Schaeffer for protein percentage. Over all models the best model fit occurred at quantile 0·50 for milk yield, fat and protein percentage, whereas, for SCS the 0·25th quantile was best. The best model to describe SCS was Dijkstra at quantiles 0·25 and 0·50, and Ali & Schaeffer at quantile 0·75. Wood function had the worst performance amongst all traits. Quantile regression is specifically appropriate for SCS which has a mixed multimodal distribution.
本研究的主要目的是比较不同的“非线性分位数回归”模型在伊朗荷斯坦奶牛产奶性状和体细胞评分（SCS）的第τ分位数（0.25、0.50和0.75）处的表现。数据由伊朗动物育种中心于1991年至2011年收集，包括183个牛群中13977头奶牛的101051条月度产奶性状和SCS记录。在分位数回归中实施了不完全伽马（伍德）、指数（威尔明克）、迪杰斯特拉和多项式（阿里和谢弗）函数。不同模型和分位数的残差均方、赤池信息准则和对数似然表明，在相同分位数下，产奶量的最佳模型是威尔明克模型，乳脂率的最佳模型是迪杰斯特拉模型，乳蛋白率的最佳模型是阿里和谢弗模型。在所有模型中，产奶量、乳脂率和乳蛋白率在分位数0.50时模型拟合最佳，而对于SCS，第0.25分位数时最佳。描述SCS的最佳模型在分位数0.25和0.50时是迪杰斯特拉模型，在分位数0.75时是阿里和谢弗模型。伍德函数在所有性状中表现最差。分位数回归特别适用于具有混合多峰分布的SCS。
