Ernst C Wit
Biographic Data
| ID | 161157 |
|---|---|
| NAME | Ernst C Wit |
| GIVEN NAMES | Ernst C |
| FAMILY NAME | Wit |
| SIGNATURE | WIT E C |
| AFFILIATIONS | Università della Svizzera italiana |
| ORCID | 0000-0002-3671-9610 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 4 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Beyond linearity and time-homogeneity
Technological advances enable the collection of many types of complex, dynamic, relational hyperevents. Despite the complexity of the data, most current Relational Hyper Event Models (RHEMs) use simple linear effects to describe the event rates. We extend the RHEM in order to allow non-linear effects that vary over time by using tensor product smooths. We validate our method on both synthetic and empirical data, examining evolving patterns and th…
Stochastic actor oriented model with random effects
Modeling non-linear effects with neural networks in Relational Event Models
Non-parametric estimation of reciprocity and triadic effects in relational event networks
Test for triadic closure and triadic protection in temporal relational event data
Factorial graphical models for dynamic networks
Dynamic network models describe many important scientific processes, from cell biology and epidemiology to sociology and finance. Estimating dynamic networks from noisy time series data is a difficult task since the number of components involved in the system is very large. As a result, the number of parameters to be estimated is typically larger than the number of observations. However, a characteristic of many real life networks is that they ar…
Factorial graphical models for dynamic networks
Dynamic network models describe many important scientific processes, from cell biology and epidemiology to sociology and finance. Estimating dynamic networks from noisy time series data is a difficult task since the number of components involved in the system is very large. As a result, the number of parameters to be estimated is typically larger than the number of observations. However, a characteristic of many real life networks is that they ar…
Test for triadic closure and triadic protection in temporal relational event data
Non-parametric estimation of reciprocity and triadic effects in relational event networks
Stochastic actor oriented model with random effects
Modeling non-linear effects with neural networks in Relational Event Models
Beyond linearity and time-homogeneity
Technological advances enable the collection of many types of complex, dynamic, relational hyperevents. Despite the complexity of the data, most current Relational Hyper Event Models (RHEMs) use simple linear effects to describe the event rates. We extend the RHEM in order to allow non-linear effects that vary over time by using tensor product smooths. We validate our method on both synthetic and empirical data, examining evolving patterns and th…
Complex Network Analysis Techniques (5 works) · Computer Science (5 works) · Psychology (4 works) · Artificial Intelligence (3 works) · Econometrics (3 works) · Event (particle physics (3 works) · Mathematics (3 works) · Mental Health Research Topics (3 works) · Opinion Dynamics and Social Influence (3 works) · Statistics (3 works)