Bayesian estimation of manufacturing effects in a fuel economy model
用分层贝叶斯回归模型分析燃油经济性数据，估计不同制造商和车型的技术利用效率，发现制造商间存在显著差异。
The analysis of fuel economy data results in estimates of the technology utilization by manufacturer and vehicle line. The analysis employs a hierarchical Bayesian regression model with random components representing vehicle lines and manufacturers. The model includes predictor variables which describe vehicle features, such as type of transmission, and vehicle line specific measurements, such as compression ratio. Non‐informative priors with novel modifications are used and the Bayes estimates are obtained by use of Gibbs sampling. The results show there is substantial variability among manufacturers in efficiently utilizing technology for fuel economy.