The bootstrap has made a fundamental impact on how we carry out statistical inference in problems without analytic solutions. This fact is illustrated with examples and comments that emphasize the parametric bootstrap and hypothesis testing.

In this paper we investigate several tests for the hypothesis of a parametric form of the error distribution in the common linear and nonparametric regression model, which are based on empirical processes of residuals. It is well known that tests in this context are not asymptotically distribution-free and the parametric bootstrap is applied to deal with this problem. The performance of the res...

This paper contains a comparison of in-sample and out-of-sample performances between the resampled efficiency technique, patented by Richard Michaud and Robert Michaud (1999), and traditional Mean-Variance portfolio selection, presented by Harry Markowitz (1952). Based on the Monte Carlo simulation, data (samples) generation process determines the algorithms by using both, parametric and nonpar...

A parametric bootstrap procedure is proposed for the mean squared error of the predictor based on a unit level model. It is demonstrated that the proposed procedure has smaller bootstrap error than a classical double bootstrap procedure with the same number of samples. Applications to a logit model under different types of auxiliary information are discussed.

AIC and BIC rankings are commonly used in model selection. When the population model is not one of the competing models, this approach leads to selection of a misspecified model and the model selected varies by sample size (Preacher & Merkle, 2012). We propose a parametric bootstrap approach for model selection. A simulation study shows that the proposed approach rejects all candidate models wh...

Ever since its introduction, the bootstrap has provided both a powerful set of solutions for practical statisticians, and a rich source of theoretical and methodological problems for statistics. In this article, some recent developments in bootstrap methodology are reviewed and discussed. After a brief introduction to the bootstrap, we consider the following topics at varying levels of detail: ...

N-of-1 study designs involve the collection and analysis of repeated measures data from an individual not using an intervention and using an intervention. This study explores the use of semi-parametric and parametric bootstrap tests in the analysis of N-of-1 studies under a single time series framework in the presence of autocorrelation. When the Type I error rates of bootstrap tests are compar...

This article describes how, in the high-level software packages used by non-statisticians, approximate non-parametric bootstrap samples can be created and analyzed without physically creating new data sets, or resorting to complex programming. The comparable performance of this shortcut method, which uses Poisson rather than multinomial frequencies for the numbers of copies of each observation,...

Uncertainty of insurance liabilities has always been the key issue in actuarial theory and practice. This is represented for instance by study and modeling of mortality in life insurance and loss distributions in traditional actuarial science. These models have evolved from early simple deterministic calculations to more sophisticated, probabilistic ones. Such probabilistic models have been tra...