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Abstract A critical task to better quantify changes in precipitation (P) mean and extreme statistics due to global warming is to gain insights into the underlying physical generating mechanisms (GMs). Here, the dominant GMs associated with daily P recorded at 2861 gauges in the Conterminous United States from 1980 to 2018 were identified from atmospheric reanalyses and publicly available datasets. The GMs include fronts (FRT), extratropical cyclones (ETC), atmospheric rivers (AR), tropical cyclones (TC), and North American Monsoon (NAM). Climatologies of the GM occurrences were developed for the nonzero P (NZP) and annual P maxima (APM) samples, characterizing the marginal and extreme P distributions, respectively. FRT is everywhere the most frequent (45-75%) GM of NZP followed by ETC (12-33%). The FRT contribution declines for APM (19-66%), which are dominated by AR (50-65%) in western regions and affected by TC (10-18%) in southern and eastern regions. The GM frequencies exhibit trends with the same signs over large regions, which are not statistically significant except for an increase in FRT (TC) frequency in the Northeast (central region). Two-sample tests showed well-defined spatial patterns with regions where (1) both the marginal and extreme P distributions of the two dominant GMs likely belong to different statistical populations, and (2) only the marginal or the extreme distributions could be considered statistically different. These results were interpreted throughL-moments and parametric distributions that adequately model NZP and APM frequency. This work provides useful insights to incorporate mixed populations and nonstationarity in P frequency analyses.
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Leveraging the Fisher Randomization Test using Confidence Distributions: Inference, Combination and Fusion Learning
Abstract The flexibility and wide applicability of the Fisher randomization test (FRT) make it an attractive tool for assessment of causal effects of interventions from modern-day randomized experiments that are increasing in size and complexity. This paper provides a theoretical inferential framework for FRT by establishing its connection with confidence distributions. Such a connection leads to development’s of (i) an unambiguous procedure for inversion of FRTs to generate confidence intervals with guaranteed coverage, (ii) new insights on the effect of size of the Monte Carlo sample on the estimation of a p-value curve and (iii) generic and specific methods to combine FRTs from multiple independent experiments with theoretical guarantees. Our developments pertain to finite sample settings but have direct extensions to large samples. Simulations and a case example demonstrate the benefit of these new developments.
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- PAR ID:
- 10398634
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Volume:
- 83
- Issue:
- 4
- ISSN:
- 1369-7412
- Format(s):
- Medium: X Size: p. 777-797
- Size(s):
- p. 777-797
- Sponsoring Org:
- National Science Foundation
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