A5103019422- Mental Health Research Topics
- Statistical Methods and Bayesian Inference
- Financial Risk and Volatility Modeling
- Pregnancy and Medication Impact
- Intergenerational and Educational Inequality Studies
- Hydrology and Drought Analysis
- Opinion Dynamics and Social Influence
- Forecasting Techniques and Applications
- Complex Systems and Decision Making
- Maternal Mental Health During Pregnancy and Postpartum
- Monetary Policy and Economic Impact
- Complex Systems and Time Series Analysis
- Animal Vocal Communication and Behavior
- Market Dynamics and Volatility
- Data Analysis with R
- Mathematical and Theoretical Epidemiology and Ecology Models
- Advanced Causal Inference Techniques
- Neural dynamics and brain function
- Energy Load and Power Forecasting
- Stochastic processes and financial applications
- Innovation Diffusion and Forecasting
- Behavioral Health and Interventions
- Insurance, Mortality, Demography, Risk Management
- Ecosystem dynamics and resilience
- Urban, Neighborhood, and Segregation Studies
Hunan Normal University
2025
Pennsylvania State University
2016-2023
ACT
2019
Intensive longitudinal data in the behavioral sciences are often noisy, multivariate nature, and may involve multiple units undergoing regime switches by showing discontinuities interspersed with continuous dynamics. Despite increasing interest using linear nonlinear differential/difference equation models switches, there has been a scarcity of software packages that fast freely accessible. We have created an R package called dynr can handle broad class discrete- continuous-time models,...
The autoregressive latent trajectory (ALT) model synthesizes the and growth curve model. ALT is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this virtue, others have cautioned that may confound interpretations model's parameters. In article, we show some—but not all—of these interpretational difficulties be clarified mathematically tested explicitly via likelihood ratio tests (LRTs) imposed on initial conditions We...
Mixture modeling is commonly used to model sample heterogeneity by identifying unobserved classes of individuals with similar characteristics. Despite abundance evidence in the literature suggesting that are often characterized different dynamic processes underlying their physiological, cognitive, psychological, and behavioral states, applications mixture surprisingly lacking. We present here a proof-of-concept example modeling, where latent groups were identified based on patterns time...
Many intensive longitudinal measurements are collected at irregularly spaced time intervals, and involve complex, possibly nonlinear heterogeneous patterns of change. Effective modelling such change processes requires continuous-time differential equation models that may be include mixed effects in the parameters. One approach fitting is to define random effect variables as additional latent a stochastic (SDE) model choice, use estimation algorithms designed for SDE models,...
Abstract In the value‐at‐risk (VaR) literature, many existing works assume that noise distribution is same over time. To take into account potential time‐varying dynamics of stock returns, we propose a dynamic asymmetric exponential distribution‐based framework. The new method includes shape parameter to control distribution, probability proportion positive and scale volatility. We combine generalized moments exponentially weighted moving average (EWMA) approach derive specifications for...