Bootstrap is an empirical non-parametric statistical technique based on data resampling that has been used to quantify uncertainties of diffusion tensor MRI (DTI) parameters, useful in tractography and in assessing DTI methods. The current bootstrap method (repetition bootstrap) used for DTI analysis performs resampling within the data sharing common diffusion gradients, requiring multiple acqu...

A large deviation result is established for the bootstrap empirical distribution in a finite sample space, thereby validating both nonparametric and parametric bootstrapping in certain phylogenetic inference problems. The bias previously observed in the bootstrap distribution of the estimated tree topology is shown to stem from dispersion effects in the joint distribution of sample and bootstra...

A semi-parametric bootstrap procedure is proposed for two-dimensional spatial processes on a finite lattice (images). Discrete wavelet transforms are used to produce coefficients that are approximately uncorrelated in space. For illustration, realizations of spatial processes from the Matérn class of spectral density functions are analyzed. The goal is to obtain bootstrap realizations by applyi...

A large deviation result is established for the bootstrap empirical distribution in a nite sample space, thereby validating both nonparametric and parametric bootstrapping in certain phylogenetic inference problems. The bias previously observed in the bootstrap distribution of the estimated tree topology is shown to stem from dispersion eeects in the joint distribution of sample and bootstrap e...

Finite mixture models are a popular tool for modelling unobserved heterogeneity. As these models are in general very complex, it is essential to have suitable methods for model diagnostics which allow e.g. to check for model identifiability, model fit and possible model restrictions. In this paper we propose to use the parametric bootstrap for model diagnostics and to visualize the bootstrap re...

In this paper we consider bootstrap approximation to the sampling distribution of the maximum likelihood estimator (m.l.e.) of the offspring mean m in a branching process with immigration. A clever modification of the standard parametric bootstrap procedure is shown to eliminate the invalidity of the standard bootstrap for the case m=1, as reported in Sriram (1992). Furthermore, the modified bo...

When the data do not come from the assumed parametric model, the usual asymptotic chisquared distribution under the null hypothesis, remains valid for “robustified” Wald and score test statistics. In this paper we compare the performance of this chi-squared approximation to that of a semiparametric bootstrap method. The bootstrap approximation is based on a onestep bootstrap estimator reflectin...

Several tests for a zero random effect variance in linear mixed models are compared. This testing problem is non-regular because the tested parameter is on the boundary of the parameter space. Size and power of the different tests are investigated in an extensive simulation study that covers a variety of important settings. These include testing for polynomial regression versus a general smooth...

Given that correct assumptions on the baseline survival function are determinant for the validity of further inferences, specific tools to test the fit of a model to real data become essential in proportional hazards models. In this sense, we have proposed a parametric bootstrap to test the fit of survival models. Monte Carlo simulations are used to generate new data sets from the estimates obt...

This paper considers the simple step-stress model from the Marshall-Olkin generalized exponential distribution when there is time constraint on the duration of the experiment. The maximum likelihood equations for estimating the parameters assuming a cumulative exposure model with lifetimes as the distributed Marshall Olkin generalized exponential are derived. The likelihood equations do not lea...