Haiyan Liu

Assistant Professior

University of California Merced

United States

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About

Dr. Haiyan Liu is an Assistant Professor at the University of California, Merced, in the United States. His research focuses on Bayesian statistical methodologies, including latent mediation analysis, model averaging for social networks, and growth mixture modeling. Dr. Liu has contributed to the academic community through numerous articles that address complex issues in statistical modeling and data analysis.

Articles (14)

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2025

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2025

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2024

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2022

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2022

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2022

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2021

Collaborators (4)

Dexin Shi

Assistant Professor

University of South Carolina

UNITED STATES

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Ren Liu

Assistant Professor

University of California Merced

UNITED STATES

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Jan-Willem Simons

Utrecht University

NETHERLANDS

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Hao Wu

Associate Professor

Vanderbilt University

UNITED STATES

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Haiyan Liu

Research Interests

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About

Articles (14)

Parameter Recovery for Misspecified Latent Mediation Models in the Bayesian Framework

Model Assumption Violations in Bayesian Latent Mediation Analysis: An Exploration of Bayesian SEM Fit Indices and PPP

A tutorial on Bayesian model averaging for exponential random graph models

Latent Variable Modeling of Social Networks with Directional Relations: An Exploration of Profile Similarity of Latent Factors

Under-Fitting and Over-Fitting: The Performance of Bayesian Model Selection and Fit Indices in SEM

Longitudinal Sentiment Analysis with Conversation Textual Data

Bayesian approach to piecewise growth mixture modeling: Issues and applications in school psychology

Applying Negative Binomial Distribution in Diagnostic Classification Models for Analyzing Count Data

Using Bayesian Piecewise Growth Curve Models to Handle Complex Nonlinear Trajectories

Poisson Diagnostic Classification Models: A Framework and an Exploratory Example

Diagnostic Classification Models for a Mixture of Ordered and Non-ordered Response Options in Rating Scales

A New Bayesian Structural Equation Modeling Approach with Priors on the Covariance Matrix Parameter

Understanding the Deviance Information Criterion for SEM: Cautions in Prior Specification

Editorial: Moving Beyond Non-informative Prior Distributions: Achieving the Full Potential of Bayesian Methods for Psychological Research

Collaborators (4)

Dexin Shi

Ren Liu

Jan-Willem Simons

Hao Wu

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