Power of the Mann–Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series

By S. Yue, P. Pilon, G. Cavadias. 2002

1,980 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/f0711c8a8828e2f5733c84eaacccc985ab6a6113

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

The stationarity paradigm revisited: Hypothesis testing using diagnostics, summary metrics, and DREAM(ABC) (2015, methodology): “Examples include the von Neumann ratio, the Kendall, Mann-Kendall, Spearman, Pearson, and Pettit tests [Buishand, 1982; Lins and Slack, 1999; Douglas et al., 2000; Yue et al., 2002; Zhang et al., 2009; Bassiouni and Oki, 2013; Roug e et al., 2013; Westra et al., 2013].”
A simulation study to examine the sensitivity of the Pettitt test to detect abrupt changes in mean (2016, result): “However, when using a Gaussian rather than gamma distribution we found that the performance was different, similar to what was found by Yue et al. (2002) for the Mann-Kendall and Spearman tests.”
A comparison of the power of the t test, Mann-Kendall and bootstrap tests for trend detection / Une comparaison de la puissance des tests t de Student, de Mann-Kendall et du bootstrap pour la détection de tendance (2004, background): “Yue et al. (2002) documented that the MK and SR tests have the same power and that their power is sensitive to the probability distribution type as well as the statistical properties of sample data.”
Monte Carlo experiments on the detection of trends in extreme values (2004, background): “Yue et al. (2002) showed that the power of the Mann–Kendall test in detecting significant trends depends on the underlying distribution.”
Innovative Trend Methodology Applications to Precipitation Records in Turkey (2017, background): “The MK test power has been investigated by Yue et al. (2002) on the bases of sample size, trend slope and type of probability distribution function (pdf).”
Exploring the ability of the Pettitt method for detecting change point by Monte Carlo simulation (2014, background): “In simulation experiments of Yue and Wang (2002) and Yue et al. (2002), the power of a test has been defined as the ratio of the number of experiments that fall in the critical region over the total number of experiments, which is given by Power ¼ Nr N where N is the total number of simulation…”
AutoLearn — Automated Feature Generation and Selection (2017, neutral): “• Pearson’s correlation [19] can only measure linear relationship whereas Spearmanś rho [20] and Kendallś tau [21] which both measure monotonic relationship, neither of them can capture all types of non-linear dependency between the two variables like distance correlation.”
On Rigorous Drought Assessment Using Daily Time Scale: Non-Stationary Frequency Analyses, Revisited Concepts, and a New Method to Yield Non-Parametric Indices (2017, background): “The higher CV, the less powerful is the trend test [29,30].”
Modelling nonlinear trend for developing non‐stationary rainfall intensity–duration–frequency curve (2017, neutral): “Further, the power of the M-K test depends on pre-assigned significance level, magnitude of the trend, sample size and the amount of variations within the time series (Yue et al., 2002).”
The distribution of Kendall's tau for testing the significance of cross-correlation in persistent data (2011, methodology): “tests such as the Pearson product-moment correlation coefficient (Yue et al. 2002).”