simes

<code>simes()</code> takes a vector of p-values corresponding to individual null
hypotheses and performs the Simes (1986) procedure for the global null
hypothesis. The global null hypothesis is the intersection of all individual
null hypotheses.

internal

An implementation of easy tools for outlier robust inference in
two-stage least squares (2SLS) models. The user specifies a reference
distribution against which observations are classified as outliers or not.
After removing the outliers, adjusted standard errors are automatically
provided. Furthermore, several statistical tests for the false outlier
detection rate can be calculated. The outlier removing algorithm can be
iterated a fixed number of times or until the procedure converges. The
algorithms and robust inference are described in more detail in Jiao (2019)
<https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.

Jonas Kurle

robust2sls

Outlier Robust Two-Stage Least Squares Inference and Testing

simes function

<dl><dt>pvals</dt>
<dd>A numeric vector of p-values corresponding to the p-values of
the individual null hypotheses.</dd>
<dt>alpha</dt>
<dd>A numeric value representing the global significance level.</dd></dl>

Arguments

Simes (1986) procedure for multiple testing — simes

<dl>

<dt>pvals</dt>
<dd>A numeric vector of p-values corresponding to the p-values of
the individual null hypotheses.</dd>


<dt>alpha</dt>
<dd>A numeric value representing the global significance level.</dd>

</dl>

simes: Simes (1986) procedure for multiple testing

Description

Usage

Value

Arguments

Details