On the relationship between the index of dispersion and Allan factor and their power for testing the Poisson assumption

Serinaldi, Francesco

doi:10.1007/s00477-013-0699-9

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…This approach is known as transformation of two random variables (e.g., (Papoulis and Pillai 2002, p. 139)), its univariate version (Z ¼ gðXÞ) has been used in several applications (e.g. Kunstmann and Kastens 2006;Ashkar and Aucoin 2011;Serinaldi 2013), and in the present case it yields Eq. 16.…”

Section: Kendall and Structure-based Return Periodsmentioning

confidence: 99%

Dismissing return periods!

Serinaldi

2014

Stoch Environ Res Risk Assess

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The concept of return period in stationary univariate frequency analysis is prone to misconceptions and misuses that are well known but still widespread. In this study we highlight how nonstationary and multivariate extensions of such a concept are affected by additional misconceptions, thus easily resulting in further ill-posed procedures and misleading conclusions. We also show that the concepts of probability of exceedance and risk of failure over a given design life period provide more coherent, general and well devised tools for risk assessment and communication.

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Section: Kendall and Structure-based Return Periodsmentioning

confidence: 99%

Dismissing return periods!

Serinaldi

2014

Stoch Environ Res Risk Assess

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230

180

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“…Our analysis showed that the data were overdispersed implying a greater variation than expected from the model [ 55 , 68 ]. Overdispersion in CJS models is often produced by data non-independence or when probabilities of detection and survival vary between individuals [ 10 ].…”

Section: Discussionmentioning

confidence: 99%

Effects of growth rate, size, and light availability on tree survival across life stages: a demographic analysis accounting for missing values and small sample sizes

Moustakas

Evans

2015

BMC Ecol

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BackgroundPlant survival is a key factor in forest dynamics and survival probabilities often vary across life stages. Studies specifically aimed at assessing tree survival are unusual and so data initially designed for other purposes often need to be used; such data are more likely to contain errors than data collected for this specific purpose.ResultsWe investigate the survival rates of ten tree species in a dataset designed to monitor growth rates. As some individuals were not included in the census at some time points we use capture-mark-recapture methods both to allow us to account for missing individuals, and to estimate relocation probabilities. Growth rates, size, and light availability were included as covariates in the model predicting survival rates. The study demonstrates that tree mortality is best described as constant between years and size-dependent at early life stages and size independent at later life stages for most species of UK hardwood. We have demonstrated that even with a twenty-year dataset it is possible to discern variability both between individuals and between species.ConclusionsOur work illustrates the potential utility of the method applied here for calculating plant population dynamics parameters in time replicated datasets with small sample sizes and missing individuals without any loss of sample size, and including explanatory covariates.Electronic supplementary materialThe online version of this article (doi:10.1186/s12898-015-0038-8) contains supplementary material, which is available to authorized users.

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“…197-198) proposed the dispersion index test for verifying the Poisson assumption for the over-threshold arrival counts. The dispersion index, d, (also known as Fano factor, Serinaldi (2013)) is the ratio between the sample variance and the sample mean, and should take values close to unity under the Poisson assumption. From asymptotic results described in Cunnane (1979); Naghettini and Pinto (2007); Silva et al (2014), 100ð1 À aÞ % confidence intervals, CI 100ð1ÀaÞ % , for d can be constructed, under the null hypothesis that the data are Poisson distributed, as follows…”

Section: Under Stationaritymentioning

confidence: 99%

On some aspects of peaks-over-threshold modeling of floods under nonstationarity using climate covariates

Silva

Naghettini

Portela

2015

Stoch Environ Res Risk Assess

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This paper discusses some aspects of flood frequency analysis using the peaks-over-threshold model with Poisson arrivals and generalized Pareto (GP) distributed peak magnitudes under nonstationarity, using climate covariates. The discussion topics were motivated by a case study on the influence of El Niño-Southern Oscillation on the flood regime in the Itajaí river basin, in Southern Brazil. The Niño3.4 (DJF) index is used as a covariate in nonstationary estimates of the Poisson and GP distributions scale parameters. Prior to the positing of parametric dependence functions, a preliminary data-driven analysis was carried out using nonparametric regression models to estimate the dependence of the parameters on the covariate. Model fits were evaluated using asymptotic likelihood ratio tests, AIC, and Q-Q plots. Results show statistically significant and complex dependence relationships with the covariate on both nonstationary parameters. The nonstationary flood hazard measure design life level (DLL) was used to compare the relative performances of stationary and nonstationary models in quantifying flood hazard over the period of records. Uncertainty analyses were carried out in every step of the application using the delta method.

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On the relationship between the index of dispersion and Allan factor and their power for testing the Poisson assumption

Cited by 11 publications

References 24 publications

Dismissing return periods!

Dismissing return periods!

Effects of growth rate, size, and light availability on tree survival across life stages: a demographic analysis accounting for missing values and small sample sizes

On some aspects of peaks-over-threshold modeling of floods under nonstationarity using climate covariates

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