bias-corrected bootstrap method bias-corrected bootstrap method(偏差校正自助法)是一种统计学方法,用于估计统计量的偏差并进行校正。该方法主要用于通过自助抽样的方式来重复抽样数据集,并通过计算抽样数据集的统计量来估计原始数据集的统计量。本文将逐步解释bias-corrected bootstrap method的基本概念、步骤和应用。
一、什么是自助法(bootstrap method): 自助法是一种通过基于已有样本数据重复抽样来估计统计量的方法。其基本思想是利用已有样本数据来模拟总体分布,并通过多次抽样计算得到统计量的分布。这种方法的主要优点是不需要对总体的分布进行假设,且可以通过构建置信区间来进行推断。 二、偏差校正: 在自助法中,得到的统计量的...
A bias-corrected bootstrap procedure for the estimation of half-life is proposed, adopting the highest density region (HDR) approach to point and interval estimation. The Monte Carlo simulation results reveal that the bias-corrected bootstrap HDR method provides an accurate point estimator, as well...
The bootstrap has become a popular method for exploring model (structure) uncertainty. Our experiments with arti,cial and real-world data demonstrate that the graphs learned from bootstrap samples can be severely biased towards too complex graphical mod-els.Accounting for this bias is hence essentia...
All previously formulated equations are not bias corrected, where the efficiency scores of DEA are subject to sampling variation of frontier (Tsolas,2011). The core idea behind the bootstrapping is to estimate the efficiency scores based on multiple sampling process (Simar & Wilson,1998). To av...
We use the bias-corrected bootstrap based on two alternative bias-correction methods: the bootstrap and an analytic formula based on asymptotic expansion. We also propose a new stationarity-correction method, based on stable spectral factorization, as an alternative to Kilian's method exclusively ...
The bootstrap has become a popular method for exploring model (structure) uncertainty. Our experiments with artificial and real-world data demonstrate that the graphs learned from bootstrap samples can be severely biased towards too complex graphical models. Accounting for this bias is hence essential...
BootstrapResamplingSuper learner algorithm can be applied to combine results of multiple base learners to improve quality of predictions. The default method for verification of super learner results is by nested cross validation. It has been proposed by Tsamardinos et al., that nested cross ...
bootstrap method/ A0250 Probability theory, stochastic processes, and statistics B0240Z Other topics in statistics C1140Z Other topics in statistics C4130 Interpolation and function approximation (numerical analysis)In this paper, we consider estimation of the mean squared prediction error (MSPE) of ...
Consequently, we emphasize the method for reducing biased of the maximum likelihood estimators (MLEs) from order (-1) to (-2). In addition, there are a biascorrected approach (BCMLE) and a bootstrap approach (BOOT). Various scenarios in Monte Carlo simulations are proceeded to compar...