This paper proposes an alternative method to deal with spatiotemporal data with censored and missing responses using the SAEM algorithm. This algorithm is a stochastic approximation of the widely used EM algorithm and is an important tool for models in which the E‐step does not have an analytic form. Besides the algorithm developed to estimate the model parameters from a likelihood‐based perspective, we present analytical expressions to compute the observed information matrix. Global influence measures are also developed and presented. Several simulation studies are conducted to examine the asymptotic properties of the SAEM estimates. The proposed method is illustrated by environmental data analysis. The computing codes are implemented in the new R package StempCens.

中文翻译：

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基于时空数据的基于似然性的推断，其审查和缺失响应

本文提出了一种使用SAEM算法处理带有删失响应的时空数据的替代方法。该算法是广泛使用的EM算法的随机近似，是E步没有解析形式的模型的重要工具。除了开发用于从基于似然的角度估计模型参数的算法外，我们还提供分析表达式来计算观察到的信息矩阵。还制定并介绍了全球影响力措施。进行了一些模拟研究，以检查SAEM估计的渐近性质。环境数据分析表明了该方法的有效性。计算代码在新的R包StempCens中实现。