This paper analyzes the finite-sample and asymptotic properties of several bootstrap and m out of n bootstrap methods for constructing confidence interval (CI) endpoints in models defined by moment inequalities. In particular, we consider using these methods directly to construct CI endpoints. By considering two very simple models, the paper shows that neither the bootstrap nor the m out of n b...

We consider fitting the so-called Emax model to continuous response data from clinical trials designed to investigate the dose-response relationship for an experimental compound. When there is insufficient information in the data to estimate all of the parameters because of the high dose asymptote being ill defined, maximum likelihood estimation fails to converge. We explore the use of either b...

A study on using non-parametric statistical methods was carried out to calculate the binding constant of an inclusion complex and to estimate its associated uncertainty. First, a correct evaluation of the stoichiometry was carried out in order to ensure an accurate determination of the binding constant. For this purpose, the modified Benesi-Hildelbrand method had been previously applied. Then, ...

This paper analyses the finite-sample and asymptotic properties of several bootstrap and m out of n bootstrap methods for constructing confidence interval (CI) endpoints in models defined by moment inequalities. In particular, we consider using these methods directly to construct CI endpoints. By considering two very simple models, the paper shows that neither the bootstrap nor the m out of n b...

We develop bootstrap methods for constructing conndence regions, including intervals and simultaneous bands, in the context of estimating the intensity function of a non-stationary Poisson process. Several diierent resampling algorithms are suggested, ranging from resampling a Poisson process with intensity equal to that estimated nonparametrically from the data, to resampling the data points t...

OBJECTIVE To compare the performance of two predictive radiologic models, logistic regression (LR) and neural network (NN), with five different resampling methods. METHODS One hundred sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross-validation, leave-one-out, a...

the bmj | BMJ 2015;350:h2622 | doi: 10.1136/bmj.h2622 1Department of Health Sciences, University of York, York YO10 5DD, UK 2Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; Correspondence to: J M Bland [email protected] Cite this as: BMJ 2015;350:h2622 doi: 10.1136/bmj.h2622 Statis...

Asymptotic chi-square tests, such as the normal theory likelihood ratio test, are often used to evaluate the goodness-of-fit of a covariance structure analysis model. Another approach is to use the bootstrap test, which is known to have the desired asymptotic level if model restrictions are taken into account in designing a resampling algorithm. The bootstrap test is, however, computationally v...

The block bootstrap confidence interval based on dependent data can outperform the computationally more convenient normal approximation only with non-trivial Studentization which, in the case of complicated statistics, calls for highly specialist treatment. We propose two different approaches to improving the accuracy of the block bootstrap confidence interval under very general conditions. The...