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A resampling technique for estimating the power of non‐parametric trend tests
Author(s) -
Nordgaard Anders,
Grimvall Anders
Publication year - 2006
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.746
Subject(s) - resampling , statistics , autoregressive model , parametric statistics , monte carlo method , autoregressive–moving average model , econometrics , series (stratigraphy) , sampling (signal processing) , computer science , rank (graph theory) , mathematics , time series , paleontology , filter (signal processing) , combinatorics , computer vision , biology
The power of Mann–Kendall tests and other non‐parametric trend tests is normally estimated by performing Monte Carlo simulations in which artificial data are generated according to simple parametric models. Here we introduce a resampling technique for power assessments that can be fully automated and accommodate almost any variation in the collected time series data. A rank regression model is employed to extract error terms representing irregular variation in data that are collected over several seasons and may contain a non‐linear trend. Thereafter, an autoregressive moving average (ARMA) bootstrap method is used to generate new time series of error terms for power simulations. A study of water quality data from two Swedish rivers illustrates how our method can provide site‐ and variable‐specific information about the power of the Hirsch and Slack test for monotonic trends. In particular, we show how to clarify the impact of sampling frequency on the power of the trend tests. Copyright © 2006 John Wiley & Sons, Ltd.