A design utility approach for preferentially sampled spatial data
针对空间优先采样问题，提出一种基于效用函数的设计分布模型，并用噪声马尔可夫链蒙特卡洛方法进行拟合，应用于氨浓度数据分析。
Abstract Spatial preferential sampling occurs when the choice of sampling locations depends stochastically on the process of interest. Ignoring this dependence leads to inaccurate inferences. Our framework models experimenter preferences jointly with the spatial process to adjust for this. We dispense with the unrealistic assumption (required by existing methods) of conditional independence of sampling locations by defining a whole design distribution proportional to a utility function on the space of designs. The proposed model likelihood is generally intractable. We provide fitting techniques based on the noisy Markov chain Monte Carlo and demonstrate their usage on a data set of spatially distributed ammonia concentrations.