Trend Detection in the Presence of Positive and Negative Serial Correlation: A Comparison of Block Maxima and Peaks‐Over‐threshold Data
Bibliographic record
Abstract
Abstract Serial correlation in a hydrometeorological time series can have deleterious effects on trend detection tests. To account for significant autocorrelation detected in datasets, various techniques have been developed over time, each having their own assumptions and accuracy. Furthermore, the existence of positive or negative serial correlation has dissimilar effects on these statistical techniques. This research compares the power and Type I error rates of various well‐known and several newer techniques to account for positive and negative serial correlation in combination with the Mann‐Kendall nonparametric trend test. The study additionally explores the application of these techniques in the presence of higher order dependence structures. Through a case study of southern Ontario watersheds, it is determined that the block maxima series (BMS) data are more likely to have significant negative lag‐1 serial correlation. Peaks‐over‐threshold (POT) data are more likely to be serially correlated and this autocorrelation is more likely to be positive. It is determined that in the case of positively serially correlated AR(1) data, block bootstrap (BBS), Hamed and Rao (1998), variance correction (VCCF1), Yue and Wang (2004), variance correction (VCCF2), and sieve bootstrap (SBS) are the most robust. Alternatively, in the case of negative AR(1) autocorrelation, the corrected trend‐free prewhitening approach (CTFPW), modified trend‐free prewhitening (MTFPW), bias corrected prewhitening (BCPW), and VCCF1 are recommended. In the presence of higher order dependence structures, VCCF1 (with all significant lags included) and VCCF2 (with all lags included) should be applied cautiously. Lastly, an assessment of the causality of the serial correlation is provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".