As with the advancement of geographical information systems, non-Gaussian spatial data sets are getting larger and more diverse. This study develops a general framework for fast and flexible non-Gaussian regression, especially for spatial/spatiotemporal modeling. The developed model, termed the compositionally-warped additive mixed model (CAMM), combines an additive mixed model (AMM) and the compositionally-warped Gaussian process to model a wide variety of non-Gaussian continuous data including spatial and other effects. A specific advantage of the proposed CAMM is that it requires no explicit assumption of data distribution unlike existing AMMs. Monte Carlo experiments show the estimation accuracy and computational efficiency of CAMM for modeling non-Gaussian data including fat-tailed and/or skewed distributions. Finally, the model is applied to crime data to examine the empirical performance of the regression analysis and prediction. The result shows that CAMM provides intuitively reasonable coefficient estimates and outperforms AMM in terms of prediction accuracy. CAMM is verified to be a fast and flexible model that potentially covers a wide variety of non-Gaussian data modeling. The proposed approach is implemented in an R package spmoran.
翻译:随着地理信息系统的发展,非加西文空间数据集正在扩大和多样化,随着地理信息系统的发展,非加西文空间数据集正在扩大和多样化,该研究为非加西文快速和灵活的非加西文回归,特别是空间/SPatota-时间模型开发了一个总体框架,开发了一种模型,称为组成扭曲的添加添加剂混合模型(CAMM)和构成扭曲的高西文进程,以模拟广泛的非加西文连续数据,包括空间和其他影响。拟议的卡米姆纳特别好处是,它不需要与现有的AMM不同,明确假设数据分布。蒙特卡洛实验显示CAMM用于非加西文数据模型的估算准确性和计算效率,包括脂肪成型和/或斜度分布。最后,该模型用于犯罪数据,以审查回归分析和预测的经验性绩效。结果显示,卡米纳米纳提供不完全合理的系数估计值,在预测准确性方面超越AMM。卡米纳米纳姆试验被核实为一种快速和灵活的模型,有可能涵盖非加西文一揽子数据的拟议模型。