In this article, we present a general framework to construct forecasts using simulation. This framework allows us to incorporate available data into a forecasting model in order to assess parameter uncertainty through a posterior distribution, which is then used to estimate a point forecast in...

Analysis of droughts from both the regional and probabilistic points of view is crucial in drought-prone arid and semiarid lands. In this work, the probabilistic behavior and spatial pattern of extreme hydrological droughts based on the minimum streamflow drought index values at 3-, 6-, 9- and...

We consider the empirical likelihood method for estimation of distribution and quantile functions where side information is incorporated through moment conditions. We systematically study the asymptotic properties of the estimators, such as the uniform strong laws of large numbers and weak...

Time series of financial asset values exhibit well-known statistical features such as heavy tails and volatility clustering. We propose a nonparametric extension of the classical Peaks-Over-Threshold method from extreme value theory to fit the time varying volatility in situations where the...

A simulation model with outcome Y=m(X) is considered, where X is an Rd-valued random variable and m:Rd→R is p-times continuously differentiable. It is shown that an importance sampling Robbins–Monro type quantile estimate achieves for 0p≤d the rate of convergence...