Model-based geostatistics (MBG) is a subfield of spatial statistics focused on predicting spatially continuous phenomena using data collected at discrete locations. Geostatistical models often rely on the assumptions of stationarity and isotropy for practical and conceptual simplicity. However, an alternative perspective involves considering non-stationarity, where statistical characteristics vary across the study area. While previous work has explored non-stationary processes, particularly those leveraging covariate information to address non-stationarity, this research expands upon these concepts by incorporating multiple covariates and proposing different ways for constructing non-stationary processes. Through a simulation study, the significance of selecting the appropriate non-stationary process is demonstrated. The proposed approach is then applied to analyse malaria prevalence data in Mozambique, showcasing its practical utility

翻译：基于模型的地质统计学（MBG）是空间统计学的一个分支领域，专注于利用离散位置收集的数据预测空间连续现象。出于实践和概念上的简便性，地质统计模型通常依赖于平稳性和各向同性的假设。然而，另一种视角则考虑非平稳性，即统计特征在研究区域内发生变化。尽管先前的研究已探索了非平稳过程，特别是那些利用协变量信息处理非平稳性的方法，但本研究通过纳入多个协变量并提出了构建非平稳过程的不同方式，对这些概念进行了扩展。通过一项模拟研究，论证了选择合适非平稳过程的重要性。随后，将所提出的方法应用于分析莫桑比克的疟疾流行率数据，展示了其实用价值。