Likelihood‐based inference for spatiotemporal data with censored and missing responses. Issue 3 (3rd December 2020) - Valeriano, Katherine A. L.; Lachos, Victor H.; Prates, Marcos O.; Matos, Larissa A.
Abstract: 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 .