Inference and testing in general point process models such as the Hawkes model is predominantly based on asymptotic approximations for likelihood-based estimators tests. As an alternative, to improve finite sample performance, this paper considers bootstrap-based inference interval estimation testing. Specifically, a wide class of we consider novel bootstrap scheme labeled ‘fixed intensity boot...

Cohen’s kappa coefficient has become a standard method for measuring the degree of agreement between two raters. Confidence intervals for kappa and weighted kappa based on its asymptotic variance are available in the SAS system through the FREQ procedure. However, this variance can become unreliable as sample size decreases or as kappa approaches unity. This paper presents a SAS macro for calcu...

We report on a numerical evaluation of the statistical bootstrap as a technique for radio-interferometric imaging fidelity assessment. The development of a fidelity assessment technique is an important scientific prerequisite for automated pipeline reduction of data from modern radio interferometers. We evaluate the statistical performance of two bootstrap methods, the model-based bootstrap and...

The Hausman test statistic in panel data models is asymptotically pivotal under the null hypothesis. It could therefore be refined using the bootstrap resampling technique. Edgeworth expansion shows that coverage of a bootstrap version of the Hausman test is second-order correct. The asymptotic vis-à-vis the bootstrap version of Hausman test are compared by Monte Carlo simulations. Results show...

In this note we consider several versions of the bootstrap and argue that it can be helpful in explaining and thinking about such procedures to use an explicit representation of the random resampling process. To illustrate the point we give such explicit representations and use them to produce some results about bootstrapping linear models that are, apparently, not widely known, at least in the...

Resampling the innovations sequence of state space models has proved to be a useful tool in many respects. For example, while under general conditions, the Gaussian MLEs of the parameters of a state space model are asymptotically normal, several researchers have found that samples must be fairly large before asymptotic results are applicable. Moreover, problems occur if the any of parameters ar...

A new resampling procedure, the continuous-path block bootstrap, is proposed in the context of testing for integrated (unit root) time series. The continuous-path block bootstrap (CBB) is a nonparametric procedure that successfully generates unit root integrated pseudo time series retaining the important characteristics of the data, e.g., the dependence structure of the stationary process drivi...

Abstract: The article proposes a computationally efficient procedure for bias adjustment in the iterated bootstrap. The new technique replaces the need for successive levels of bootstrap resampling by proposing an approximation for the double bootstrap “calibrating coefficient” using only one draw from the second level probability distribution. Extensive Monte Carlo evidence suggest that the pr...