论文标题
通过逻辑多元汽车在空间依赖的混合模型之前
Spatially dependent mixture models via the Logistic Multivariate CAR prior
论文作者
论文摘要
我们考虑了在空间依赖的面积数据的问题，在每个区域中都有独立观测值，并建议通过高斯分布的有限混合物对每个区域的密度进行建模。空间依赖性是通过新的关节分布引入的，用于单纯形中的向量集合，我们称其为logisticmcar。我们表明，可以通过分析描述LogisticMCAR分布的显着特征，并且基于Pólya-Gamma身份的合适的增强方案允许得出有效的Markov链蒙特卡洛算法。与竞争对手相比，我们的模型已证明可以更好地估计不同特征不同（断开连接）位置的密度。我们讨论了阿姆斯特丹市Airbnb列表的真实数据集上的一个应用程序，还展示了如何轻松地将其合并到模型中的其他协变量信息。
We consider the problem of spatially dependent areal data, where for each area independent observations are available, and propose to model the density of each area through a finite mixture of Gaussian distributions. The spatial dependence is introduced via a novel joint distribution for a collection of vectors in the simplex, that we term logisticMCAR. We show that salient features of the logisticMCAR distribution can be described analytically, and that a suitable augmentation scheme based on the Pólya-Gamma identity allows to derive an efficient Markov Chain Monte Carlo algorithm. When compared to competitors, our model has proved to better estimate densities in different (disconnected) areal locations when they have different characteristics. We discuss an application on a real dataset of Airbnb listings in the city of Amsterdam, also showing how to easily incorporate for additional covariate information in the model.
