贝叶斯空间分位数回归
Bayesian Spatial Quantile Regression
摘要: 自Koenker和Bassett在1978年提出分位数回归模型以来，分位数回归已经取得了长足的发展，尤其是随着信息技术的深入普及，分位数回归的研究和应用成为统计学和现代计量经济学的研究热点之一。但考虑到传统的分位数回归方法在空气污染问题研究中的不足，本文尝试在传统分位数模型中加入空间影响因素，并结合贝叶斯理论，建立贝叶斯空间分位数模型，对影响我国城市空气质量的主要因素进行分析。
Abstract: Since Koenker and Bassett proposed the quantile regression model in 1978, quantile regression has made significant progress. Especially with the widespread adoption of information technology, the research and application of quantile regression has become a research hotspot in statistics and modern econometrics. However, considering the shortcomings of traditional quantile regression methods in air pollution research, this paper attempts to incorporate spatial influencing factors into the traditional quantile model and, combined with Bayesian theory, establish a Bayesian spatial quantile model to analyze the main factors affecting urban air quality in my country.
文章引用：丁玉璐. 贝叶斯空间分位数回归[J]. 理论数学, 2026, 16(5): 204-214. https://doi.org/10.12677/pm.2026.165144

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