Timeline for Extreme Value Analysis - Nonrandom/Preferential Sampling
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
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| Jan 5 at 10:42 | comment | added | user225256 | Suppose you think 40 of your measurements came from the top half of the distribution and 20 came from the bottom half. Then create a new dataset by repeating each of your 20 lowest observations twice. This is now be a dataset of 80 observations with basically uniform sampling, and you can perform an extreme value analysis on that. | |
| Nov 30, 2023 at 19:19 | history | edited | kjetil b halvorsen♦ |
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| Oct 16, 2023 at 6:37 | comment | added | Yves | Welcome to CV. Maybe you could use a non-stationary Peaks Over Threshold (POT) framework, in which the parameters depend (smoothly) on an index $s$ representing the coordinate along the pipeline. A Bayesian predictive approach of this model also seems relevant here since it seems that you have some prior information on the dependence and want to predict the maximum. | |
| S Oct 13, 2023 at 9:20 | review | First questions | |||
| Oct 13, 2023 at 9:37 | |||||
| S Oct 13, 2023 at 9:20 | history | asked | In the Limit | CC BY-SA 4.0 |