A Simulation Comparison of Several Procedures for Testing the Poisson Assumption

Karlis, Dimitris; Xekalaki, Evdokia

doi:10.1111/1467-9884.00240

Cited by 38 publications

(21 citation statements)

References 75 publications

(77 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Because of sampling variability associated with a finite sample, we want to test whether the dispersion coefficient is statistically different from 1. To accomplish this task we use a Monte Carlo approach (e.g., see Karlis and Xekalaki [2000] for an overview on tests for the Poisson distribution). For each station, we generate B = 5000 synthetic series drawn from a Poisson distribution with the same mean and the same length of the record of interest.…”

Section: Methodsmentioning

confidence: 99%

Annual maximum and peaks-over-threshold analyses of daily rainfall accumulations for Austria

Villarini¹,

Smith²,

Ntelekos³

et al. 2011

J. Geophys. Res.

View full text Add to dashboard Cite

[1] Daily rainfall data from 31 rain gauges in Austria with a record of at least 50 years are used to perform annual maximum and peaks-over-threshold analyses to address question concerning: (1) temporal nonstationarities in extreme rainfall; (2) dependence of extreme rainfall on elevation; (3) changes in the frequency, duration, and accumulation of extreme rainfall; and (4) relation between the occurrence of extreme rainfall and the North Atlantic Oscillation (NAO). Nonparametric tests are used to check for the presence of abrupt and slowly varying changes in annual maximum daily rainfall. The Pettitt test is used to detect change-points in the mean and variance of the quantity of interest, while Mann-Kendall and Spearman tests are used to detect monotonically increasing or decreasing patterns. Annual maximum daily rainfall from these 31 Austrian rain gauges provides limited evidence of the violation of the stationarity assumption, and we cannot make conclusive statements about the presence of a possible climate change signal. The parameters of the Generalized Extreme Value (GEV) distribution are used to examine the dependence of extreme rainfall on elevation. The results of this study do not suggest a strong relation between elevation and the GEV parameters. Changes over time in the frequency, duration, and intensity of extreme rainfall days, defined as days exceeding the 95th, 99th, and 99.5th percentiles, are tested using monotonic trend tests and change-point tests. There is little evidence of changes in these quantities. By modeling the frequency of extreme rainfall days in terms of the NAO with a Poisson regression model, we found that NAO is a more significant predictor of the occurrence of extreme rainfall days in western Austria.

show abstract

Section: Methodsmentioning

confidence: 99%

Annual maximum and peaks-over-threshold analyses of daily rainfall accumulations for Austria

Villarini¹,

Smith²,

Ntelekos³

et al. 2011

J. Geophys. Res.

View full text Add to dashboard Cite

show abstract

“…A coefficient of dispersion different from 1 does not, however, necessarily mean that the assumption of Poisson distribution is not valid because of the sampling uncertainties associated with estimates of the mean and variance from a finite sample. To test whether the empirical coefficient of dispersion is statistically significantly different from 1, a Monte-Carlo approach is used (Karlis and Xekalaki, 2000 for an overview on tests for the Poisson distribution). For each station, B = 5000 synthetic series are generated from a Poisson distribution with the same mean and record length, and B coefficients of dispersion are computed.…”

Section: Poisson Regression Modelmentioning

confidence: 99%

Analyses of extreme flooding in Austria over the period 1951–2006

Villarini

Smith

Serinaldi

et al. 2011

Intl Journal of Climatology

View full text Add to dashboard Cite

ABSTRACT:Analyses of extreme flooding in Austria is performed using daily discharge time series from 27 stations over the period . The main research questions revolve around: (1) temporal non-stationarities in the flood record, (2) upper tail and scaling properties of the flood peak records, and (3) relation between magnitude and frequency of flooding and the North Atlantic Oscillation (NAO). Two datasets are derived from the daily discharge time series: annual maximum daily discharge and peaks-over-threshold (POT) data. The validity of the stationarity assumption in the annual maximum discharge record is assessed by investigating the presence of abrupt and slowly varying changes using nonparametric tests. The time series are tested for abrupt changes both in the mean and variance of the flood peak distributions by means of the Pettitt test. The presence of monotonic trends is investigated by means of the Mann-Kendall and Spearman tests. Violations of the stationarity assumption are associated with abrupt rather than gradual changes. These step changes generally involve river regulation through construction of dams or other major engineering works. It is not possible to make conclusive statements about the presence of an anthropogenic climate change signal in the flood peak record. Similar conclusions are obtained when focussing on the frequency of POT floods. The Generalised Extreme Value distribution is used to study the upper tail and scaling properties of annual maximum daily discharge records. The location and scale parameters exhibit power-law behaviour as a function of drainage area. The shape parameters indicate that the flood peak distributions for Austria have a heavy tail. Non-stationary modelling of the annual maximum daily discharge and POT time series is used to explore the relation between flood magnitude and frequency and NAO. The results indicate that NAO is a significant covariate in explaining the magnitude and frequency of occurrence of flooding over a large part of Austria.

show abstract

“…To test for this possibility before exploring more complicated causes of community composition we performed an index of dispersion test on all plant and AMF species used in the study. For a dataset x with n elements, this statistic is (n ¡ 1) £ var(x)/ mean(x) and its asymptotic distribution is 2 with n ¡ 1 df (Karlis and Xekalaki 2000;PotthoV and Whitting 1966). A P-value is obtained by determining the cumulative density of the 2 (n ¡ 1) distribution to the right of the test statistic, and represents the probability that the observed variance arose by chance from a Poisson distribution.…”

Section: Statisticsmentioning

confidence: 99%

Scale-dependent niche axes of arbuscular mycorrhizal fungi

2008

View full text Add to dashboard Cite

Arbuscular mycorrhizal fungi (AMF) are mutualistic with most species of plants and are known to influence plant community diversity and composition. To better understand natural plant communities and the ecological processes they control it is important to understand what determines the distribution and diversity of AMF. We tested three putative niche axes: plant species composition, disturbance history, and soil chemistry against AMF species composition to determine which axis correlated most strongly with a changing AMF community. Due to a scale dependency we were not able to absolutely rank their importance, but we did find that each correlated significantly with AMF community change at our site. Among soil properties, pH and NO(3) were found to be especially good predictors of AMF community change. In a similar analysis of the plant community we found that time since disturbance had by far the largest impact on community composition.

show abstract

A Simulation Comparison of Several Procedures for Testing the Poisson Assumption

Cited by 38 publications

References 75 publications

Annual maximum and peaks-over-threshold analyses of daily rainfall accumulations for Austria

Annual maximum and peaks-over-threshold analyses of daily rainfall accumulations for Austria

Analyses of extreme flooding in Austria over the period 1951–2006

Scale-dependent niche axes of arbuscular mycorrhizal fungi

Contact Info

Product

Resources

About