get.difference.matrix

Given a data matrix and a reference column index, construct the difference matrix
used in hypothesis testing procedures. Each column represents the difference
between the reference dimension and one of 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.difference.matrix function

<dl><dt>data</dt>
<dd>A <code>n</code> by <code>p</code> data matrix; each row is a <code>p</code>-dimensional sample.</dd>
<dt>r</dt>
<dd>An integer between 1 and <code>p</code>, indicating the reference column (dimension).</dd></dl>

Arguments

Construct a difference matrix for argmin hypothesis testing — get.difference.matrix

<dl>

<dt>data</dt>
<dd>A <code>n</code> by <code>p</code> data matrix; each row is a <code>p</code>-dimensional sample.</dd>


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

</dl>

get.difference.matrix: Construct a difference matrix for argmin hypothesis testing

Description

Usage

Value

Arguments

Examples