bs_mean

Compute a bootstrap distribution for the sample mean.

Implements modern resampling and permutation methods for robust
statistical inference without restrictive parametric assumptions. Provides
bias-corrected and accelerated (BCa) bootstrap (Efron and Tibshirani (1993)
<doi:10.1201/9780429246593>), wild bootstrap for heteroscedastic regression
(Liu (1988) <doi:10.1214/aos/1176351062>, Davidson and Flachaire (2008)
<doi:10.1016/j.jeconom.2008.08.003>), block bootstrap for time series
(Politis and Romano (1994) <doi:10.1080/01621459.1994.10476870>), and
permutation-based multiple testing correction (Westfall and Young (1993)
<ISBN:0-471-55761-7>). Methods handle non-normal data,
heteroscedasticity, time series correlation, and multiple comparisons.

Ibrahim Kholil Rakib

modernBoot

Modern Resampling Methods: Bootstraps, Wild, Block, Permutation,
and Selection Guidance

bs_mean function

<dl><dt>x</dt>
<dd>numeric vector of data.</dd>
<dt>R</dt>
<dd>integer number of bootstrap replicates (default 2000).</dd>
<dt>conf</dt>
<dd>numeric confidence level between 0 and 1 (default 0.95).</dd></dl>

Arguments

Nonparametric Bootstrap Confidence Interval for the Mean — bs_mean

<dl>

<dt>x</dt>
<dd>numeric vector of data.</dd>


<dt>R</dt>
<dd>integer number of bootstrap replicates (default 2000).</dd>


<dt>conf</dt>
<dd>numeric confidence level between 0 and 1 (default 0.95).</dd>

</dl>

bs_mean: Nonparametric Bootstrap Confidence Interval for the Mean

Description

Usage

Value

Arguments

Examples