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 ...

nowadays, high-voltage circuit breakers have reached such high short-circuit capabilities that testing them under the full rated voltage is generally not possible with direct tests, and they are conducted by using the synthetic test methods. although the phenomena associated with making tests is of particular importance especially in case of load break switches, but making tests are rather disr...

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...

The introduction of our paper discusses the leave-one-out and cross-validation error estimators. In our implementation of cross-validation, we use k = 5 folds and 5 repetitions, each with different partitions. The basic bootstrap zero estimator, ε̂b0, [3], [4] generates B bootstrap samples, each consisting of n equally-likely draws with replacement from the original sample of size n. Each bootst...

SUMMARY This paper first provides a critical review on some existing methods for estimating prediction error in classifying microarray data where the number of genes greatly exceeds the number of specimen. Special attention is given to the bootstrap-related methods. When the sample size n is small, we find that all the reviewed methods suffer from either substantial bias or variability. We intr...