The stable distribution, in its many parametrizations, is central to many stochastic processes. Many random variables that occur in the study of Lévy processes are related to it. Good progress has been made recently for simulating various quantities related to the stable law. In this note, we...

An efficient Monte Carlo method for random sample generation from high dimensional distributions of complex structures is developed. The method is based on random discretization of the sample space and direct inversion of the discretized cumulative distribution function. It requires only the...

We present a black-box style rejection method that is valid for generating random variables with any log-concave density, provided that one knows a mode of the density. When the density is only known up to a constant factor, that method is no longer applicable.

This paper deals with the prediction of time series with correlated errors at each time point using a Bayesian forecast approach based on the multivariate Holt–Winters model. Assuming that each of the univariate time series comes from the univariate Holt–Winters model, all of them sharing a...

There exists a vast literature on nonuniform random variate generators. Most of these generators are especially designed for a particular distribution. However, in pratice only a few of these are available to practioners. Moreover, for problems as (e.g.) sampling from the truncated normal...

A nonparametric technique for estimating the cumulative intensity function of a nonhomogeneous Poisson process from one or more realizations on an interval is extended here to include realizations that overlap. This technique does not require any arbitrary parameters from the modeler, and the...

A nonparametric technique for estimating the cumulative intensity function of a nonhomogeneous Poisson process from one or more realizations is developed. This technique does not require any arbitrary parameters from the modeler, and the estimated cumulative intensity function can be used to...