The problem of estimating censored linear regression models with autocorrelated errors arises in many environmental and social studies. The present work proposes a Bayesian approach to estimate censored regression models with AR(p) errors. The algorithm developed here considers the Gibbs sampler with data augmentation(GDA), in which, at each iteration, both the model parameters and the latent variables are sampled. The data augmentation is achieved from multiple sampling of the latent variables from the corresponding conditional distributions. A suitable variable transformation allows the full likelihood to be obtained. A simulation study indicates that the proposed approach produces estimates with a high accuracy even in scenarios where the proportion of censored observations is large. The method is further illustrated in a real data of cloud ceiling height, including model checking and selection for censored time series data.
翻译:在许多环境和社会研究中都出现了估算带有与机算有关的错误的经审查的线性回归模型的问题。本项工作提出了一种贝叶西亚方法,用AR(p)错误来估算经审查的回归模型。此处开发的算法考虑了带有数据增强(GDA)的Gibbs取样器,在每次迭代中,都对模型参数和潜在变量进行了抽样。数据增强是通过从相应的有条件分布中对潜在变量进行多次取样而实现的。适当的变量转换使得完全有可能获得数据。一个模拟研究表明,拟议的方法产生的估计数非常精确,即使在经过审查的观测比例很大的情况下也是如此。这种方法在云天高的实际数据中得到了进一步说明,其中包括对经过审查的时间序列数据进行模型检查和选择。