get.sample.mean.r

Computes the vector of differences between the sample mean at the reference index
and the remaining dimensions.

internal

Provides methods to construct frequentist confidence sets with valid marginal
coverage for identifying the population-level argmin or argmax based on IID data.
For instance, given an n by p loss matrix—where n is the sample size and p is the
number of models—the CS.argmin() method produces a discrete confidence set that contains
the model with the minimal (best) expected risk with desired probability. The argmin.HT()
method helps check if a specific model should be included in such a confidence set. The main
implemented method is proposed by Tianyu Zhang, Hao Lee and Jing Lei (2024)
"Winners with confidence: Discrete argmin inference with an application to model selection".

Hao Lee

argminCS

Argmin Inference over a Discrete Candidate Set

Tianyu Zhang

Jing Lei

get.sample.mean.r function

<dl><dt>sample.mean</dt>
<dd>A vector of length <code>p</code> containing the sample means of each dimension.</dd>
<dt>r</dt>
<dd>An integer between 1 and <code>p</code>, indicating the reference dimension.</dd></dl>

Arguments

Compute sample mean differences for hypothesis testing — get.sample.mean.r

<dl>

<dt>sample.mean</dt>
<dd>A vector of length <code>p</code> containing the sample means of each dimension.</dd>


<dt>r</dt>
<dd>An integer between 1 and <code>p</code>, indicating the reference dimension.</dd>

</dl>

get.sample.mean.r: Compute sample mean differences for hypothesis testing

Description

Usage

Value

Arguments

Examples