get.quantile.gupta.selection

Generate the quantile used for the selection procedure in gupta.1965argminCS by Monte Carlo estimation.

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.quantile.gupta.selection function

<dl><dt>p</dt>
<dd>The number of dimensions in your data matrix.</dd>
<dt>alpha</dt>
<dd>The level of the upper quantile; defaults to 0.05 (95% percentile).</dd>
<dt>N</dt>
<dd>The number of Monte Carlo repetitions; defaults to 100000.</dd></dl>

Arguments

Generate the quantile used for the selection procedure in gupta.1965argminCS. — get.quantile.gupta.selection

<dl>

<dt>p</dt>
<dd>The number of dimensions in your data matrix.</dd>


<dt>alpha</dt>
<dd>The level of the upper quantile; defaults to 0.05 (95% percentile).</dd>


<dt>N</dt>
<dd>The number of Monte Carlo repetitions; defaults to 100000.</dd>

</dl>

get.quantile.gupta.selection: Generate the quantile used for the selection procedure in gupta.1965argminCS.

Description

Usage

Value

Arguments

References

Examples