Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model [0.03%]
基于二元时间序列日常数据的稀有事件和非单调缺失值建模：贝叶斯选择模型
Sun-Joo Cho,Autumn Kujawa,Corinne Carlton et al.
Sun-Joo Cho et al.
This study investigates the relationship between daily interpersonal stress (binary, time-varying) and suicidal behavior (binary, time-varying) using 90 days of daily diary data from 106 adolescents assessed immediately after discharge from...
Jan O Bauer
Jan O Bauer
Yvonnick Noel
Yvonnick Noel
User-Friendly Software and Estimated Conditional Standard Errors of Measurement. A Commentary on Pfadt et al [0.03%]
关于用户友好型软件和估计条件标准误差的评论_pfadt等人的研究分析
L Andries van der Ark
L Andries van der Ark
Bayesian Modeling and Inference for Item Response Model with Nonignorable Missing Data [0.03%]
非忽略缺失数据下的项目反应模型的贝叶斯推断方法研究
Jing Wu,Zhihua Ma,Ming-Hui Chen
Jing Wu
Variable Selection via Knockoffs in Missing Data Settings with Categorical Predictors [0.03%]
基于分类预测变量的缺失数据设置中的 Knockoffs 变量选择方法
Silvia Bacci,Emanuela Dreassi,Leonardo Grilli et al.
Silvia Bacci et al.
Large-scale assessment data typically include numerous variables, often affected by missing values. Motivated by the challenges arising in this framework, we extend the knockoffs method for selecting predictors to settings with missing valu...
Edgar C Merkle
Edgar C Merkle
Estimating Discrete Latent Variable Models Using Amortized Variational Inference [0.03%]
基于推移变分推理估计离散潜变量模型
Karel Veldkamp,Raoul Grasman,Dylan Molenaar
Karel Veldkamp
Recent research shows that amortized variational inference (AVI) can be used to efficiently estimate high-dimensional latent variable models on large datasets. However, its use has remained limited to item response theory (IRT), and general...