In this paper two nonparametric tests for monotonic trend detection are compared with respect to their power and accuracy. The importance of comparing powers at equal empirical signiicance levels rather than nominal levels, is stressed. Therefore, an appropriate graphical method is presented. The eeect of the sampling frequency is also assessed using a trajectory representation that visualizes ...

The Mann-Kendall test is a commonly-used nonparametric test for time trend. However, the standard P-values obtained from it are based on an assumption of independence between observations (since the theory is that of the Kendall correlation). However, observations in time series are often autocorrelated: knowing that one observation is larger than the mean may tell you that the next observation...

Kendall Rank Correlation Let (X1, Y1), · · · , (Xn, Yn) be a bivariate random sample of size n. The Pearson correlation coefficient provides an optimal measure of the degree of association between the X’s and the Y ’s when the sample is drawn from a bivariate normal distribution. The Pearson correlation coefficient is reasonably robust for many other distributions as well. The Kendall correlati...

Global warming is a result of the greenhouse effect caused by gases released into atmosphere; It usually expressed as an increase in measured average temperatures. In addition to this, extreme weather events such droughts, floods, and severe hurricanes become more frequent, specified severe. The effects global climate change on hydrological meteorological variables are increasing day day. There...

in this investigation, using mann – kendall graphical-statistical test, which is one of the proposed methods of world meteorological organization (wmo) for time series analysis, the change trend of long – term mean of annual minimum and maximum temperatures in iran will be studied. to evaluate this trend and access a suitable pattern in this situation, average of annual minimum and maximum ...

Discovering promising research themes in a scientific domain by evaluating semantic information extracted from bibliometric databases represents challenging task for Natural Language Processing (NLP). While existing NLP methods generally characterize the topics using unique key terms, we take step further more accurately modeling as finite sets of terms. The proposed approach involves two stage...