This study presents a scalable Bayesian estimation algorithm for sparse estimation in exploratory item factor analysis based on a classical Bayesian estimation method, namely Bayesian joint modal estimation (BJME). BJME estimates the model parameters and factor scores that maximize the complete-data joint posterior density. Simulation studies show that the proposed algorithm has high computational efficiency and accuracy in variable selection over latent factors and the recovery of the model parameters. Moreover, we conducted a real data analysis using large-scale data from a psychological assessment that targeted the Big Five personality traits. This result indicates that the proposed algorithm achieves computationally efficient parameter estimation and extracts the interpretable factor loading structure.

翻译：本研究基于经典贝叶斯估计方法——贝叶斯联合众数估计（BJME），提出了一种可扩展的贝叶斯估计算法，用于探索性项目因子分析中的稀疏估计。BJME通过最大化完整数据的联合后验密度来估计模型参数和因子得分。模拟研究表明，所提算法在潜在因子的变量选择与模型参数恢复方面具有较高的计算效率和准确性。此外，我们利用针对大五人格特质的心理评估大规模数据进行了实证分析。结果表明，该算法能够实现计算高效的参数估计，并提取出可解释的因子载荷结构。