We consider the performance of the bootstrap in high-dimensions for the setting of linear regression, where p < n but p/n is not close to zero. We consider ordinary least-squares as well as robust regression methods and adopt a minimalist performance requirement: can the bootstrap give us good confidence intervals for a single coordinate of ? (where is the true regression vector). We show throu...

Bürger and Cubasch (BC) set out to reassess the skill of “proxy-based reconstructions of Northern [H]emisphere temperature” by applying several variants of reconstruction methods to samples of proxy data and instrumental temperature data generated by a resampling technique. There are several open questions about statistical methods for climate reconstructions—for example, questions about the ma...

We report on a broader evaluation of statistical bootstrap resampling methods as a tool for pixel-level calibration and imaging fidelity assessment in radio interferometry. Pixel-level imaging fidelity assessment is a challenging problem, important for the value it holds in robust scientific interpretation of interferometric images, enhancement of automated pipeline reduction systems needed to ...

We present a random probability distribution which approximates, in the sense of weak convergence, the Dirichlet process and supports a Bayesian resampling plan called a proper Bayesian bootstrap. 2000 Mathematics Subject Classification: 62G09, 60B10.

Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial transfer entropy (PTE), an information and model-free measure, is used. Two resampling techniques, t...

Resampling techniques are used widely within the ERP community to assess statistical significance and especially in the deception detection literature. Here we argue that because of statistical bias, bootstrap should not be used in combination with methods like peak – to –peak. Instead permutation tests provide a more appropriate alternative. Keywords: bootstrap, permutation, significance testi...

MOTIVATION Protein sequence comparison methods are routinely used to infer the intricate network of evolutionary relationships found within the rapidly growing library of protein sequences, and thereby to predict the structure and function of uncharacterized proteins. In the present study, we detail an improved statistical benchmark of pairwise protein sequence comparison algorithms. We use boo...

We study a sieve bootstrap procedure for time series with a deterministic trend. The sieve for constructing the bootstrap is based on autoregressive a p p r o ximation. Given time series data, one would rst use a preliminary estimate of the trend of the underlying time series and then approximate the noise process by a large autoregressive model of increasing order as the sample size grows. The...

The bootstrap is a popular and powerful method for assessing precision of estimators and inferential methods. However, for massive datasets which are increasingly prevalent, the bootstrap becomes prohibitively costly in computation and its feasibility is questionable even with modern parallel computing platforms. Recently Kleiner, Talwalkar, Sarkar, and Jordan (2014) proposed a method called BL...