rdirich_dk_vects

Sample independently from Dirichlet distributions for each of d_k vectors

We consider the problem where we observe k vectors (possibly of different lengths), each representing an independent multinomial random vector. For a given function that takes in the concatenated vector of multinomial probabilities and outputs a real number, this is a Monte Carlo estimation procedure of an exact p-value and confidence interval. The resulting inference is valid even in small samples, when the parameter is on the boundary, and when the function is not differentiable at the parameter value, all situations where asymptotic methods and the bootstrap would fail. For more details see Sachs, Fay, and Gabriel (2025) <doi:10.48550/arXiv.2406.19141>.

Michael Sachs

xactonomial

Inference for Functions of Multinomial Parameters

Michael C Sachs

Michael P Fay

Erin E Gabriel

David B Dahl

rdirich_dk_vects function

<dl><dt>nsamp</dt>
<dd>number of samples to take</dd>
<dt>alpha</dt>
<dd>List of vectors of concentration parameters</dd></dl>

Arguments

Sample independently from Dirichlet distributions for each of d_k vectors — rdirich_dk_vects

<dl>

<dt>nsamp</dt>
<dd>number of samples to take</dd>


<dt>alpha</dt>
<dd>List of vectors of concentration parameters</dd>

</dl>

rdirich_dk_vects: Sample independently from Dirichlet distributions for each of d_k vectors

Description

Usage

Value

Arguments

Examples