{'fraction_non_alphanumeric': 0.020066889632107024, 'fraction_numerical': 0.0, 'mean_word_length': 5.655555555555556, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Feature Significance in Geostatistics

Geographically referenced data are routinely smoothed using kriging or spline methodology. Features in the resulting surface such as peaks, inclines, ridges, and valleys are often of interest. This article develops inference for the significance of such features through extension of methodology for univariate features known as SiZer. We work with low rank radial spline smoothers. These allow the handling of sparse designs, large sample sizes, and simulation-based critical value approximation. We illustrate the methodology on two geostatistical datasets.