Error Analysis for Small-Sample, High-Variance Data: Cautions for Bootstrapping and Bayesian Bootstrapping
نویسندگان
چکیده
منابع مشابه
Bootstrapping Sample Quantiles of Discrete Data
Sample quantiles are consistent estimators for the true quantile and satisfy central limit theorems (CLTs) if the underlying distribution is continuous. If the distribution is discrete, the situation is much more delicate. In this case, sample quantiles are known to be not even consistent in general for the population quantiles. In a motivating example, we show that Efron’s bootstrap does not c...
متن کاملBootstrapping the Stein Variance Estimator
This paper applies the bootstrap methods proposed by Efron (1979) to the Stein variance estimator proposed by Stein (1964). It is shown by Monte Carlo experiments that the parametric bootstrap yields the considerable accurate estimates of mean, standard error and confidence limits of the Stein variance estimator.
متن کاملBlock-bootstrapping for noisy data.
BACKGROUND Statistical inference of signals is key to understand fundamental processes in the neurosciences. It is essential to distinguish true from random effects. To this end, statistical concepts of confidence intervals, significance levels and hypothesis tests are employed. Bootstrap-based approaches complement the analytical approaches, replacing the latter whenever these are not possible...
متن کاملCross-validation and bootstrapping are unreliable in small sample classification
0167-8655/$ see front matter 2008 Elsevier B.V. A doi:10.1016/j.patrec.2008.06.018 * Corresponding authors. Address: Department o University, Academic Hospital, SE-751 85 Uppasala, Sw fax: +46 18 611 37 03. E-mail addresses: [email protected] (A angstrom.uu.se (M.G. Gustafsson). The interest in statistical classification for critical applications such as diagnoses of patient samples ...
متن کاملAsymptotic algorithm for computing the sample variance of interval data
The problem of the sample variance computation for epistemic inter-val-valued data is, in general, NP-hard. Therefore, known efficient algorithms for computing variance require strong restrictions on admissible intervals like the no-subset property or heavy limitations on the number of possible intersections between intervals. A new asymptotic algorithm for computing the upper bound of the samp...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Biophysical Journal
سال: 2019
ISSN: 0006-3495
DOI: 10.1016/j.bpj.2018.11.779