VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.
Modelling Dependent Data (University of Zurich)
Last Updated: 2026-06-01 11:33:51
Abstract
In many applications, the basic assumption of independent random quantities is unrealistic and appropriate procedures are necessary to model the dependencies. According to the type of dependency, different approaches are commonly used.
Objective
After a successful completion, the student is capable of 1. differentiating and characterizing different types of longitudinal and spatial data 2. proposing, fitting and interpreting standard models for longitudinal and spatial data 3. understanding and interpreting the results of complex models.
Content
The lecture starts with a focus on an important case of dependent data: so-called longitudinal and time series data, these are in general repeated measurements over time from the same individual/observational unit. Subsequently, spatial data are studied, starting with so-called lattice data and introducing conditional and simultaneous autoregressive (CAR and SAR) models as well as Gaussian Markov random fields. Further, classical geostatistical spatial processes are introduced and methods for estimation and prediction explored as well as some extensions discussed. Finally, students will learn how to model spatial point patterns, e.g. counts of cases of a disease over a geographical region. Theoretical concepts, practical applications and implementations (in R) are balanced throughout the semester.
General Information
- Language
- English
- Levels
- MSC
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Modelling Dependent Data (University of Zurich)
Does not take place this semester.
**Course at University of Zurich**
|
No time listed | 3 h weekly |
Offered In
-
Statistik Master (Die hier aufgelisteten Lehrveranstaltungen gehören zum Curriculum des Master-Studiengangs Statistik. Die entsprechenden KP gelten nicht als Mobilitäts-KP, auch wenn gewisse Lerneinheiten nicht an der ETH Zürich belegt werden können.)