An extension of the basic local independence model to multiple observed classifications [0.03%]
基本局部独立模型在多个观察分类下的扩展
Pasquale Anselmi,Debora de Chiusole,Egidio Robusto et al.
Pasquale Anselmi et al.
The basic local independence model (BLIM) is appropriate in situations where populations do not differ in the probabilities of the knowledge states and the probabilities of careless errors and lucky guesses of the items. In some situations,...
A Bayes factor framework for unified parameter estimation and hypothesis testing [0.03%]
贝叶斯因子框架统一参数估计和假设检验
Samuel Pawel
Samuel Pawel
The Bayes factor, the data-based updating factor of the prior to posterior odds of two hypotheses, is a natural measure of statistical evidence for one hypothesis over the other. We show how Bayes factors can also be used for parameter esti...
Detecting Critical Change in Dynamics Through Outlier Detection with Time-Varying Parameters [0.03%]
基于时变参数的异常值检测在动力学中的关键变化检测
Meng Chen,Michael D Hunter,Sy-Miin Chow
Meng Chen
Intensive longitudinal data are often found to be non-stationary, namely, showing changes in statistical properties, such as means and variance-covariance structures, over time. One way to accommodate non-stationarity is to specify key para...
Residual permutation tests for feature importance in machine learning [0.03%]
机器学习中残差置换检验的特征重要性评估方法研究
Po-Hsien Huang
Po-Hsien Huang
Psychological research has traditionally relied on linear models to test scientific hypotheses. However, the emergence of machine learning (ML) algorithms has opened new opportunities for exploring variable relationships beyond linear const...
Joint analysis of dispersed count-time data using a bivariate latent factor model [0.03%]
基于二元潜在因素模型的分散型计数时间数据联合分析方法研究
Cornelis J Potgieter,Akihito Kamata,Yusuf Kara et al.
Cornelis J Potgieter et al.
In this study, we explore parameter estimation for a joint count-time data model with a two-factor latent trait structure, representing accuracy and speed. Each count-time variable pair corresponds to a specific item on a measurement instru...
Mark de Rooij,Lorenza Cotugno,Roberta Siciliano
Mark de Rooij
In this paper, we propose the generalized mixed reduced rank regression method, GMR3 for short. GMR3 is a regression method for a mix of numeric, binary and ordinal response variables. The predictor variables can be a mix of binary, nominal...
IRT-based response style models and related methodology: Review and commentary [0.03%]
基于项目反应理论的回应倾向性模型及相关的研究方法：综述与评论
Daniel M Bolt,Lionel Meng
Daniel M Bolt
We provide a review and commentary on recent methodological research related to item response theory (IRT) modelling of response styles in psychological measurement. Our review describes the different categories of IRT models that have been...
A tutorial on Bayesian model averaging for exponential random graph models [0.03%]
贝叶斯模型平均在指数随机图模型中的应用教程
Ihnwhi Heo,Jan-Willem Simons,Haiyan Liu
Ihnwhi Heo
The use of exponential random graph models (ERGMs) is becoming prevalent in psychology due to their ability to explain and predict the formation of edges between vertices in a network. Valid inference with ERGMs requires correctly specifyin...
A tutorial for understanding SEM using R: Where do all the numbers come from? [0.03%]
用R语言理解结构方程模型的教程：所有的数字都是从哪儿来的？
Yves Rosseel,Marc Vidal
Yves Rosseel
Structural equation modeling (SEM) is often seen as a complex and difficult method, especially for those who want to understand how the numbers in SEM software output are actually computed. Although many open-source SEM tools are now availa...
Bhargab Chattopadhyay,Sudeep R Bapat
Bhargab Chattopadhyay
Effect size estimates are now widely reported in various behavioural studies. In precise estimation or power analysis studies, sample size planning revolves around the standard error (or variance) of the effect size. Note these studies are ...