In this work we propose a semiparametric bivariate copula whose density is defined by a picewise constant function on disjoint squares. We obtain the maximum likelihood estimators which reduce to the sample copula under specific conditions. We carry out a full Bayesian analysis of the model and propose a spatial dependent prior distribution for the model parameters. This prior allows the parameters to borrow strength across neighbouring regions to produce smooth posterior estimates. We implement a simulation study and illustrate the performance of our model with a real dataset.

翻译：在这项工作中,我们建议采用半对数双差相交共生体,其密度由脱节方形上的一个Picewith恒定函数确定。我们获得最大可能性的估测器,在特定条件下将样本相交共生体压缩到样本中。我们对该模型进行全面的巴伊西亚分析,并提议模型参数的根据空间的先前分布。这之前允许参数在邻近区域借用强度,以得出光滑的后方估计值。我们进行了模拟研究,用真实的数据集来说明模型的性能。