mean_psi

<code>mean_psi</code> function for sample expectation of psi

Implementation of assumption-lean and data-adaptive post-prediction inference (POPInf), for valid and efficient statistical inference based on data predicted by machine learning. See Miao, Miao, Wu, Zhao, and Lu (2023) <arXiv:2311.14220>.

Jiacheng Miao

POPInf

Assumption-Lean and Data-Adaptive Post-Prediction Inference

mean_psi function

<dl><dt>X</dt>
<dd>Array or DataFrame containing covariates</dd>
<dt>Y</dt>
<dd>Array or DataFrame of outcomes</dd>
<dt>theta</dt>
<dd>parameter theta</dd>
<dt>quant</dt>
<dd>quantile for quantile estimation</dd>
<dt>method</dt>
<dd>indicates the method to be used for M-estimation. Options include "mean", "quantile", "ols", "logistic", and "poisson".</dd></dl>

Arguments

Sample expectation of psi — mean_psi

<dl>

<dt>X</dt>
<dd>Array or DataFrame containing covariates</dd>


<dt>Y</dt>
<dd>Array or DataFrame of outcomes</dd>


<dt>theta</dt>
<dd>parameter theta</dd>


<dt>quant</dt>
<dd>quantile for quantile estimation</dd>


<dt>method</dt>
<dd>indicates the method to be used for M-estimation. Options include "mean", "quantile", "ols", "logistic", and "poisson".</dd>

</dl>

mean_psi: Sample expectation of psi

Description

Usage

Value

Arguments