Using a deterministic framework allows us to estimate a function with the purpose of interpolating data in spatial statistics. Radial basis functions are commonly used for scattered data interpolation in a d-dimensional space, however, interpolation problems have to deal with dense matrices. For the case of smoothing thin plate splines, we propose an efficient way to address this problem by compressing the dense matrix by an hierarchical matrix ($\mathcal{H}$-matrix) and using the conjugate gradient method to solve the linear system of equations. A simulation study was conducted to assess the effectiveness of the spatial interpolation method. The results indicated that employing an $\mathcal{H}$-matrix along with the conjugate gradient method allows for efficient computations while maintaining a minimal error. We also provide a sensitivity analysis that covers a range of smoothing and compression parameter values, along with a Monte Carlo simulation aimed at quantifying uncertainty in the approximated function. Lastly, we present a comparative study between the proposed approach and thin plate regression using the "mgcv" package of the statistical software R. The comparison results demonstrate similar interpolation performance between the two methods.
Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University（哈瑟尔特大学交叉大学生物统计学和统计生物信息学研究所（I-BioStat）数据科学研究所）；Department of Biostatistics, Erasmus University Medical Center（伊拉斯姆斯大学医学中心生物统计学系）
