estimate_Bay_CV_SE_jackknife_individual

This file contains the jackknife resampling functions
for the MultiLevelOptimalBayes package.

internal

Jackknife

Bayesian

parameters

errors

standard

with

estimate

individual-level

Implements a regularized Bayesian estimator that optimizes the estimation
of between-group coefficients for multilevel latent variable models by minimizing
mean squared error (MSE) and balancing variance and bias. The package provides more reliable
estimates in scenarios with limited data, offering a robust solution for accurate
parameter estimation in two-level latent variable models. It is designed for
researchers in psychology, education, and related fields who face challenges in
estimating between-group effects under small sample sizes and low intraclass
correlation coefficients. The package includes comprehensive S3 methods for result
objects: print(), summary(), coef(), se(), vcov(), confint(), as.data.frame(),
dim(), length(), names(), and update() for enhanced usability and integration
with standard R workflows. Dashuk et al. (2025a) <doi:10.1017/psy.2025.10045>
derived the optimal regularized Bayesian estimator;
Dashuk et al. (2025b) <doi:10.1007/s41237-025-00264-7> extended it to
the multivariate case; and Luedtke et al. (2008) <doi:10.1037/a0012869>
formalized the two-level latent variable framework.

Valerii Dashuk

MultiLevelOptimalBayes

Regularized Bayesian Estimator for Two-Level Latent Variable
Models

Binayak Timilsina

Martin Hecht

Steffen Zitzmann

estimate_Bay_CV_SE_jackknife_individual function

<dl><dt>data</dt>
<dd>List containing data for jackknife estimation</dd></dl>

Arguments

Jackknife Resampling Functions for MultiLevelOptimalBayes — estimate_Bay_CV_SE_jackknife_individual

<dl>

<dt>data</dt>
<dd>List containing data for jackknife estimation</dd>

</dl>

Jackknife Resampling Functions for MultiLevelOptimalBayes

estimate_Bay_CV_SE_jackknife_individual: Jackknife Resampling Functions for MultiLevelOptimalBayes

Description

Usage

Value

Arguments