In this paper, we examine the validity of non-parametric spatial bootstrap as a procedure to quantify errors in estimates of N -point correlation functions. We do this by means of a small simulation study with simple point process models and estimating the two-point correlation functions and their errors. The coverage of confidence intervals obtained using bootstrap is compared with those obtai...

This paper proposes a misspecification-robust iid bootstrap for the generalized method of moment estimators and establishes asymptotic refinements of the symmetric percentile-t bootstrap confidence interval. The paper extends results of Hall and Horowitz (1996) and Andrews (2002). In particular, the proposed method does not involve recentering the moment function in implementing the bootstrap, ...

In this paper we propose a vectorized implementation of the non-parametric bootstrap for statistics based on sample moments. Basically, we adopt the multinomial sampling formulation of the non-parametric bootstrap, and compute bootstrap replications of sample moment statistics by simply weighting the observed data according to multinomial counts instead of evaluating the statistic on a resample...

Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subject (p) is much larger than the number of subjects (n), calculating and storing the leading principal components from each bootstrap sample can be computationally infeasible. To address this, we outline methods for fast...

In this paper we propose a consistent and asymptotically normal estimator (CAN) of intensities 1  , 2  for a queueing network with feedback (in which a job may return to previously visited nodes) with distribution-free inter-arrival and service times. Using this estimator and its estimated variance, some   100 1 %   asymptotic confidence intervals of intensities are constructed. Also boot...

We use Monte Carlo methods to study the properties of the bootstrap Breusch-Godfrey test for autocorrelated errors in two versions a) by bootstrapping under the null hypothesis, restricted and b) by bootstrapping under the alternative hypothesis, unrestricted. We use the residual bootstrap for the bootstrap-BG test. Our analysis regarding the size of the test reveals that both bootstrap tests h...

For independent data, non-parametric bootstrap is realised by resampling the data with replacement. This approach fails for dependent data such as time series. If the data generating process is at least stationary and mixing, the blockwise bootstrap by drawing subsamples or blocks of the data saves the concept. For the blockwise bootstrap a blocklength has to be selected. We propose a method fo...

In nonregular smooth function models with vanishing first derivative, the conventional bootstrap is known to be inconsistent, whereas the m out of n bootstrap is consistent. We explore the effects of iterating the m out of n bootstrap on coverage accuracy of bootstrap percentile confidence intervals in such models, and develop a special iterative scheme which outperforms the non-iterated m out ...

Bipartition support in maximum-likelihood (ML) analysis is most commonly assessed using the nonparametric bootstrap. Although bootstrap replicates should theoretically be analyzed in the same manner as the original data, model selection is almost never conducted for bootstrap replicates, substitution-model parameters are often fixed to their maximum-likelihood estimates (MLEs) for the empirical...