23 citations · 43 across the 15 of their papers we have counts for
17 papers
Measuring Negotiation Traits with Collaborative Choice Data: A Two-Stage Latent Variable Model
Tongxin Zhang, Yunxiao Chen, Jiangang Hao +4
Consider a collaborative decision-making setting in which team members with different reward structures need to make a joint decision. Repeated collaborative decisions can reflect…
DAG-CLIP: A DAG Learning Framework in the Presence of Latent Variables
Siliang Zhang, Yunxiao Chen, Irini Moustaki
Learning directed acyclic graphs (DAGs) to uncover causal mechanisms has attracted substantial attention in machine learning. While most existing methods focus exclusively on obser…
Sparse Latent Class Analysis For Dichotomous Responses: Post-Estimation Refinement via Item-level Pseudo-Likelihood
Yuxuan Xu, Lea Kaufmann, Yunxiao Chen +2
Latent Class Analysis (LCA) is widely used to identify unobserved subgroups in social and behavioural sciences. A long-standing challenge for LCA is the interpretability of the lat…
A Generalized Additive Partial-Mastery Cognitive Diagnosis Model
Camilo Cárdenas-Hurtado, Sze Ming Lee, Yunxiao Chen +1
Cognitive diagnosis models (CDMs) are restricted latent class models widely used to measure attributes of interest in diagnostic assessments across education, psychology, biomedica…
Maximum softly penalised likelihood in factor analysis
Philipp Sterzinger, Ioannis Kosmids, Irini Moustaki
Estimation in exploratory factor analysis often yields estimates on the boundary of the parameter space. Such occurrences, known as Heywood cases, are characterised by non-positive…
Unfolding the Network of Peer Grades: A Latent Variable Approach
Giuseppe Mignemi, Yunxiao Chen, Irini Moustaki
Peer grading is an educational system in which students assess each other's work. It is commonly applied under Massive Open Online Course (MOOC) and offline classroom settings. Wit…
Boosting Stochastic Optimisation for High-dimensional Latent Variable Models
Motonori Oka, Yunxiao Chen, Irini Moustaki
Latent variable models are widely used in social and behavioural sciences, including education, psychology, and political science. With the increasing availability of large and com…
The generalized Hausman test for detecting non-normality in the latent variable distribution of the two-parameter IRT model
Lucia Guastadisegni, Silvia Cagnone, Irini Moustaki +1
This paper introduces the generalized Hausman test as a novel method for detecting non-normality of the latent variable distribution of unidimensional Item Response Theory (IRT) mo…
Pairwise likelihood estimation and limited information goodness-of-fit test statistics for binary factor analysis models under complex survey sampling
Haziq Jamil, Irini Moustaki, Chris Skinner
This paper discusses estimation and limited information goodness-of-fit test statistics in factor models for binary data using pairwise likelihood estimation and sampling weights.…
When Composite Likelihood Meets Stochastic Approximation
Giuseppe Alfonzetti, Ruggero Bellio, Yunxiao Chen +1
A composite likelihood is an inference function derived by multiplying a set of likelihood components. This approach provides a flexible framework for drawing inference when the li…
DIF Analysis with Unknown Groups and Anchor Items
Gabriel Wallin, Yunxiao Chen, Irini Moustaki
Ensuring fairness in instruments like survey questionnaires or educational tests is crucial. One way to address this is by a Differential Item Functioning (DIF) analysis, which exa…
Rotation to Sparse Loadings using Losses and Related Inference Problems
Xinyi Liu, Gabriel Wallin, Yunxiao Chen +1
Researchers have widely used exploratory factor analysis (EFA) to learn the latent structure underlying multivariate data. Rotation and regularised estimation are two classes of me…
Sequential Bayesian Inference for Factor Analysis
Konstantinos Vamvourellis, Konstantinos Kalogeropoulos, Irini Moustaki
We develop an efficient Bayesian sequential inference framework for factor analysis models observed via various data types, such as continuous, binary and ordinal data. In the cont…
Model Assessment for a Generalised Bayesian Structural Equation Model
Konstantinos Vamvourellis, Konstantinos Kalogeropoulos, Irini Moustaki
The paper proposes a novel model assessment paradigm aiming to address shortcoming of posterior predictive values, which provide the default metric of fit for Bayesian structur…
A Note on Likelihood Ratio Tests for Models with Latent Variables
Yunxiao Chen, Irini Moustaki, Haoran Zhang
The likelihood ratio test (LRT) is widely used for comparing the relative fit of nested latent variable models. Following Wilks' theorem, the LRT is conducted by comparing the LRT…
Detection of Two-Way Outliers in Multivariate Data and Application to Cheating Detection in Educational Tests
Yunxiao Chen, Yan Lu, Irini Moustaki
The paper proposes a new latent variable model for the simultaneous (two-way) detection of outlying individuals and items for item-response-type data. The proposed model is a syner…
Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions
Silia Vitoratou, Ioannis Ntzoufras, Irini Moustaki
In latent variable models the parameter estimation can be implemented by using the joint or the marginal likelihood, based on independence or conditional independence assumptions.…