Sebastian Castro-Alvarez
Biographic Data
| ID | 8867123 |
|---|---|
| NAME | Sebastian Castro-Alvarez |
| GIVEN NAMES | Sebastian |
| FAMILY NAME | Castro-Alvarez |
| SIGNATURE | CASTRO-ALVAREZ S |
| AFFILIATIONS | University of Groningen |
| ORCID | 0000-0002-1326-0827 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Improving Subgrouping Accuracy in Dynamic Network Models Through Feature Selection
Dynamic psychological processes often vary across individuals. Identifying subgroups of individuals with differential person-specific dynamics is of great interest in health and behavioral science
A Time-Varying Dynamic Partial Credit Model to Analyze Polytomous and Multivariate Time Series Data
The accessibility to electronic devices and the novel statistical methodologies available have allowed researchers to comprehend psychological processes at the individual level. However, there are still great challenges to overcome as, in many cases, collected data are more complex than the available models are able to handle. For example, most methods assume that the variables in the time series are measured on an interval scale, which is not th…
Mixed-Effects Trait-State-Occasion Model
The trait-state-occasion model (TSO) is a popular model within the latent state-trait theory (LST). The TSO allows distinguishing the trait and the state components of the psychological constructs measured in longitudinal data, while also taking into account the carry-over effects between consecutive measurements. In the present study, we extend a multilevel version of the TSO model to allow for the combination of fixed and random situations, nam…
No prominent works on this page.
Mixed-Effects Trait-State-Occasion Model
The trait-state-occasion model (TSO) is a popular model within the latent state-trait theory (LST). The TSO allows distinguishing the trait and the state components of the psychological constructs measured in longitudinal data, while also taking into account the carry-over effects between consecutive measurements. In the present study, we extend a multilevel version of the TSO model to allow for the combination of fixed and random situations, nam…
A Time-Varying Dynamic Partial Credit Model to Analyze Polytomous and Multivariate Time Series Data
The accessibility to electronic devices and the novel statistical methodologies available have allowed researchers to comprehend psychological processes at the individual level. However, there are still great challenges to overcome as, in many cases, collected data are more complex than the available models are able to handle. For example, most methods assume that the variables in the time series are measured on an interval scale, which is not th…
Improving Subgrouping Accuracy in Dynamic Network Models Through Feature Selection
Dynamic psychological processes often vary across individuals. Identifying subgroups of individuals with differential person-specific dynamics is of great interest in health and behavioral science
Computer Science (3 works) · Data mining (3 works) · Econometrics (2 works) · Machine learning (2 works) · Mathematics (2 works) · Mental Health Research Topics (2 works) · Statistics (2 works) · Advanced Statistical Modeling Techniques (1 work) · Algorithms and Data Compression (1 work) · Artificial Intelligence (1 work)