scholarly journals Which catchment characteristics control the temporal dependence structure of daily river flows?

2014 ◽  
Vol 29 (6) ◽  
pp. 1353-1369 ◽  
Author(s):  
Andrew Chiverton ◽  
Jamie Hannaford ◽  
Ian Holman ◽  
Ron Corstanje ◽  
Christel Prudhomme ◽  
...  
2010 ◽  
Vol 40 (1) ◽  
pp. 123-150 ◽  
Author(s):  
Hélène Cossette ◽  
Etienne Marceau ◽  
Véronique Maume-Deschamps

AbstractIn this paper, we consider various specifications of the general discrete-time risk model in which a serial dependence structure is introduced between the claim numbers for each period. We consider risk models based on compound distributions assuming several examples of discrete variate time series as specific temporal dependence structures: Poisson MA(1) process, Poisson AR(1) process, Markov Bernoulli process and Markov regime-switching process. In these models, we derive expressions for a function that allow us to find the Lundberg coefficient. Specific cases for which an explicit expression can be found for the Lundberg coefficient are also presented. Numerical examples are provided to illustrate different topics discussed in the paper.


2020 ◽  
Vol 23 (5) ◽  
pp. 1248-1273
Author(s):  
Luisa Beghin ◽  
Janusz Gajda

Abstract Fractional relaxation equations, as well as relaxation functions time-changed by independent stochastic processes have been widely studied (see, for example, [21], [33] and [11]). We start here by proving that the upper-incomplete Gamma function satisfies the tempered-relaxation equation (of index ρ ∈ (0, 1)); thanks to this explicit form of the solution, we can then derive its spectral distribution, which extends the stable law. Accordingly, we define a new class of selfsimilar processes (by means of the n-times Laplace transform of its density) which is indexed by the parameter ρ: in the special case where ρ = 1, it reduces to the stable subordinator. Therefore the parameter ρ can be seen as a measure of the local deviation from the temporal dependence structure displayed in the standard stable case.


Author(s):  
Marta Nai Ruscone ◽  
Daniel Fernández

AbstractThe HDI (Human Development Index) is a widely used index based on the average of measures of health, education, and income. It assesses the progress of countries worldwide. The publicly available data set associated with the HDI can be seen as a table with 3 dimensions (three-way table): countries, indexes regarding progress, and years (from 2010 to 2018). Thus, modeling the serial dependence structure of this type of intricate three-way tables is a challenge. D-vine copulas are a special class of multivariate copulas that are particularly suited for modeling serial dependence. This work aims to assess the evolution of the dependence relationship between the indexes of the HDI data set over time through D-vine copulas, which has not been fully used before in the area, as far as we are concerned. We tested our approach to European and African countries and compare their results.


2008 ◽  
Author(s):  
Jeanmarie Haney ◽  
Dale Turner ◽  
Vashti Supplee

Sign in / Sign up

Export Citation Format

Share Document