Bootstrap predictive inference for ARIMA processes

Bayesian Nonparametric Predictive Inference and Bootstrap Techniques

Abst rac t . We address the question as to whether a prior distribution on the space of distribution functions exists which generates the posterior produced by Efron's and Rubin's bootstrap techniques, emphasizing the connections with the Dirichlet process. We also introduce a new resampling plan which has two advantages: prior opinions are taken into account and the predictive distribution of ...

Fractional ARIMA Processes and Its

Exact Statistical Inference for Some Parametric Nonhomogeneous Poisson Processes

Nonhomogeneous Poisson processes (NHPPs) are often used to model recurrent events, and there is thus a need to check model fit for such models. We study the problem of obtaining exact goodness-of-fit tests for certain parametric NHPPs, using a method based on Monte Carlo simulation conditional on sufficient statistics. A closely related way of obtaining exact confidence intervals in parametri...

Bootstrap for continuous - time processes

An Edgeworth expansion of a Studentized statistic for an ergodic regenerative strong Markov process is validated. A specific nonparametric bootstrap method is proposed and proved to be second-order correct in the light of the Edgeworth expansion, which is a variant of the regenerative block bootstrap designed for discrete-time Markov processes. One-dimensional diffusions and semi-Markov process...

Bootstrap Inference in Econometrics

The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas of bootstrap inference. The paper discusses Monte Carlo tests, several types of bootstrap test, an...