Tile low-rank approximations of non-Gaussian space and space-time Tukey g-and-h random field likelihoods and predictions on large-scale systems

• Published in: Journal of parallel and distributed computing Vol. 180; p. 104715
• Main Authors: Mondal, Sagnik, Abdulah, Sameh, Ltaief, Hatem, Sun, Ying, Genton, Marc G., Keyes, David E.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2023
• Subjects: Climate/weather data; Large-scale geostatistical modeling; Likelihood approximation; Non-Gaussian random field; Tile Low-Rank (TLR) approximation; Tile Low-Rank (TLR) approximation; Climate/weather data; Likelihood approximation; Non-Gaussian random field; Large-scale geostatistical modeling

Large-scale statistical modeling has become necessary with the vast flood of geospace data coming from various sources. In space statistics, the Maximum Likelihood Estimation (MLE) is widely considered for modeling geospace data by estimating a set of statistical parameters related to a predefined c...