Large datasets are daily gathered from different remote sensing platforms and statistical models are usually used to combine them by accounting for spatially varying bias corrections. The statistical inference of these models is usually based on Markov chain Monte Carlo (MCMC) samplers which involve updating a high-dimensional random effect vector and hence present slow mixing and convergence. To overcome this and enable fast inference in big spatial data problems, we propose the recursive nearest neighbor co-kriging (RNNC) model and use it as a framework which allows us to develop two computationally efficient inferential procedures: a) the collapsed RNNC that reduces the posterior sampling space by integrating out the latent processes, and b) the conjugate RNNC which is an MCMC free inference that significantly reduces the computational time without sacrificing prediction accuracy. The good computational and predictive performance of our proposed algorithms are demonstrated on benchmark examples and the analysis of the High-resolution Infrared Radiation Sounder data gathered from two NOAA polar orbiting satellites in which we managed to reduce the computational time from multiple hours to just a few minutes.
翻译:每天从不同的遥感平台收集大数据集,通常使用统计模型来计算空间差异的偏差校正。这些模型的统计推断通常以Markov链Monte Carlo(MCMC)取样器为基础,这些取样器涉及更新一个高维随机效应矢量,从而造成缓慢的混合和趋同。为了克服这一点,并能够在大空间数据问题中快速推断出,我们提议采用相距较近的循环近邻联合操纵模型(RNNC)模型,并将其作为一个框架,使我们能够开发两个计算效率高的推断程序:(a) 崩溃的RNNC,通过整合潜伏过程减少远地点取样空间;和(b) 共和RNNC,这是一个MC自由推断器,在不牺牲预测准确性的情况下大大减少计算时间。我们拟议算法的良好计算和预测性性表现在基准示例上以及对从两颗NOAA极轨道卫星收集到的高分辨率红外辐射测深线数据的分析中,我们设法将计算时间从多小时减少到几分钟。</s>