Xinyuan Song
Biographic Data
| ID | 6040984 |
|---|---|
| NAME | Xinyuan Song |
| GIVEN NAMES | Xinyuan |
| FAMILY NAME | Song |
| SIGNATURE | SONG X |
| AFFILIATIONS | Chinese University of Hong Kong |
| ORCID | 0000-0002-4877-3200 |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 4 |
| WORKS AS AUTHOR | 13 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2007 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Bayesian Structural Equation Multi-State Model for Mediation Analysis
This study introduces a joint modeling framework that integrates structural equation model (SEM) with multi-state semi-competing risks model to investigate the complex causal mechanism among exposure, latent mediators, and multiple hierarchically correlated survival outcomes. The framework first employs a linear SEM to characterize latent mediators underlying highly correlated observable surrogates. These latent mediators are then incorporated in…
Semiparametric Structural Equation Models with Interval-Censored Data
Structural equation models offer a valuable tool for delineating the complicated interrelationships among multiple variables, including observed and latent variables. Over the last few decades, structural equation models have successfully analyzed complete and right-censored survival data, exemplified by wide applications in psychological, social, or genomic studies. However, the existing methodology for structural equation modeling is not concer…
Causal Mediation Analysis for Multivariate Longitudinal Data and Survival Outcomes
This study proposes a joint modeling approach to conduct causal mediation analysis that accommodates multivariate longitudinal data, dynamic latent mediator, and survival outcome. First, we introduce a confirmatory factor analysis model to characterize a time-varying latent mediator through multivariate longitudinal observable variables. Then, we establish a growth curve model to describe the linear trajectory of the dynamic latent mediator and s…
Bayesian Quantile Latent Factor on Image Regression
This paper considers a quantile latent factor-on-image (Q-LoI) regression model to comprehensively investigate the relationship between the latent factor of interest and scalar and imaging predictors at different quantiles. The latent factor is characterized by several manifest variables through a confirmatory factor analysis model and then regressed on scalar and imaging covariates. We propose a two-stage method to conduct statistical inference.…
Joint Deep Learning for Covariate Decomposition and Treatment Effect Estimation
This study proposes a joint deep learning method, namely, confounding factor joint decomposition under counterfactual regression (CFJD-CFR), to identify a minimum adjustment covariate set and estimate the average treatment effect (ATE). CFJD-CFR includes two levels: feature learning and prediction model. Feature learning constructs the objective function based on clear variable attribute recognition criteria to identify and decompose a covariate …
Generalized Structural Equation Model with Survival Outcomes and Time-Varying Coefficients
The conventional Cox proportional hazards (PH) model typically assumes fully observed predictors and constant regression coefficients. However, some predictors are latent variables, each of which must be characterized by multiple observed indicators from various perspectives. Moreover, the predictor effects may vary with time in practice. Accommodating such latent variables and identifying temporal covariate effects are frequently of primary inte…
Joint Hidden Markov Model for Longitudinal and Time-to-Event Data with Latent Variables
This study develops a new joint modeling approach to simultaneously analyze longitudinal and time-to-event data with latent variables. The proposed model consists of three components. The first component is a hidden Markov model for investigating a longitudinal observation process and its underlying transition process as well as their potential risk factors and dynamic heterogeneity. The second component is a factor analysis model for characteriz…
Bayesian Causal Mediation Analysis with Latent Mediators and Survival Outcome
This study develops a joint modeling approach that incorporates latent traits into causal mediation analysis with multiple mediators and a survival outcome. A linear structural equation model is used to characterize the latent mediators with several highly correlated observable surrogates and depicts the relationships among multiple parallel or causally ordered mediators and the exposure. A proportional hazards model is used to derive the path-sp…
Bayesian Two-level Model for Repeated Partially Ordered Responses
Given the questionnaire design and the nature of the problem, partially ordered data that are neither completely ordered nor completely unordered are frequently encountered in social, behavioral, and medical studies. However, early developments in partially ordered data analysis are very limited and restricted only to cross-sectional data. In this study, we propose a Bayesian two-level regression model for analyzing repeated partially ordered res…
Bayesian Diagnostics of Hidden Markov Structural Equation Models with Missing Data
Cocaine is a type of drug that functions to increase the availability of the neurotransmitter dopamine in the brain. However, cocaine dependence or abuse is highly related to an increased risk of psychiatric disorders and deficits in cognitive performance, attention, and decision-making abilities. Given the chronic and persistent features of drug addiction, the progression of abstaining from cocaine often evolves across several states, such as ad…
Bayesian Adaptive Lasso for Ordinal Regression With Latent Variables
We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct simultaneous estimation and variable selection. Nice features including empirical performance of the …
The association between visual impairment and the risk of mortality
Discriminant Analysis Using Mixed Continuous, Dichotomous, and Ordered Categorical Variables
This article proposes an intuitive approach for predictive discriminant analysis with mixed continuous, dichotomous, and ordered categorical variables that are defined via an underlying multivariate normal distribution with a threshold specification. The classification rule is based on the comparison of the observed data logarithm probability density functions. To reduce the computational burden, the analysis is conducted in the context of a conf…
Bayesian Adaptive Lasso for Ordinal Regression With Latent Variables
We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct simultaneous estimation and variable selection. Nice features including empirical performance of the …
The association between visual impairment and the risk of mortality
Discriminant Analysis Using Mixed Continuous, Dichotomous, and Ordered Categorical Variables
This article proposes an intuitive approach for predictive discriminant analysis with mixed continuous, dichotomous, and ordered categorical variables that are defined via an underlying multivariate normal distribution with a threshold specification. The classification rule is based on the comparison of the observed data logarithm probability density functions. To reduce the computational burden, the analysis is conducted in the context of a conf…
The association between visual impairment and the risk of mortality
Bayesian Adaptive Lasso for Ordinal Regression With Latent Variables
We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct simultaneous estimation and variable selection. Nice features including empirical performance of the …
Bayesian Diagnostics of Hidden Markov Structural Equation Models with Missing Data
Cocaine is a type of drug that functions to increase the availability of the neurotransmitter dopamine in the brain. However, cocaine dependence or abuse is highly related to an increased risk of psychiatric disorders and deficits in cognitive performance, attention, and decision-making abilities. Given the chronic and persistent features of drug addiction, the progression of abstaining from cocaine often evolves across several states, such as ad…
Bayesian Causal Mediation Analysis with Latent Mediators and Survival Outcome
This study develops a joint modeling approach that incorporates latent traits into causal mediation analysis with multiple mediators and a survival outcome. A linear structural equation model is used to characterize the latent mediators with several highly correlated observable surrogates and depicts the relationships among multiple parallel or causally ordered mediators and the exposure. A proportional hazards model is used to derive the path-sp…
Bayesian Two-level Model for Repeated Partially Ordered Responses
Given the questionnaire design and the nature of the problem, partially ordered data that are neither completely ordered nor completely unordered are frequently encountered in social, behavioral, and medical studies. However, early developments in partially ordered data analysis are very limited and restricted only to cross-sectional data. In this study, we propose a Bayesian two-level regression model for analyzing repeated partially ordered res…
Joint Hidden Markov Model for Longitudinal and Time-to-Event Data with Latent Variables
This study develops a new joint modeling approach to simultaneously analyze longitudinal and time-to-event data with latent variables. The proposed model consists of three components. The first component is a hidden Markov model for investigating a longitudinal observation process and its underlying transition process as well as their potential risk factors and dynamic heterogeneity. The second component is a factor analysis model for characteriz…
Causal Mediation Analysis for Multivariate Longitudinal Data and Survival Outcomes
This study proposes a joint modeling approach to conduct causal mediation analysis that accommodates multivariate longitudinal data, dynamic latent mediator, and survival outcome. First, we introduce a confirmatory factor analysis model to characterize a time-varying latent mediator through multivariate longitudinal observable variables. Then, we establish a growth curve model to describe the linear trajectory of the dynamic latent mediator and s…
Bayesian Quantile Latent Factor on Image Regression
This paper considers a quantile latent factor-on-image (Q-LoI) regression model to comprehensively investigate the relationship between the latent factor of interest and scalar and imaging predictors at different quantiles. The latent factor is characterized by several manifest variables through a confirmatory factor analysis model and then regressed on scalar and imaging covariates. We propose a two-stage method to conduct statistical inference.…
Joint Deep Learning for Covariate Decomposition and Treatment Effect Estimation
This study proposes a joint deep learning method, namely, confounding factor joint decomposition under counterfactual regression (CFJD-CFR), to identify a minimum adjustment covariate set and estimate the average treatment effect (ATE). CFJD-CFR includes two levels: feature learning and prediction model. Feature learning constructs the objective function based on clear variable attribute recognition criteria to identify and decompose a covariate …
Generalized Structural Equation Model with Survival Outcomes and Time-Varying Coefficients
The conventional Cox proportional hazards (PH) model typically assumes fully observed predictors and constant regression coefficients. However, some predictors are latent variables, each of which must be characterized by multiple observed indicators from various perspectives. Moreover, the predictor effects may vary with time in practice. Accommodating such latent variables and identifying temporal covariate effects are frequently of primary inte…
Semiparametric Structural Equation Models with Interval-Censored Data
Structural equation models offer a valuable tool for delineating the complicated interrelationships among multiple variables, including observed and latent variables. Over the last few decades, structural equation models have successfully analyzed complete and right-censored survival data, exemplified by wide applications in psychological, social, or genomic studies. However, the existing methodology for structural equation modeling is not concer…
Bayesian Structural Equation Multi-State Model for Mediation Analysis
This study introduces a joint modeling framework that integrates structural equation model (SEM) with multi-state semi-competing risks model to investigate the complex causal mechanism among exposure, latent mediators, and multiple hierarchically correlated survival outcomes. The framework first employs a linear SEM to characterize latent mediators underlying highly correlated observable surrogates. These latent mediators are then incorporated in…
Econometrics (11 works) · Mathematics (11 works) · Statistics (10 works) · Computer Science (9 works) · Statistical Methods and Inference (9 works) · Artificial Intelligence (8 works) · Bayesian probability (8 works) · Latent variable (7 works) · Structural equation modeling (7 works) · Statistical Methods and Bayesian Inference (6 works)