28
Sep
![Teaser image to StatTag and StatWrap for Conducting Collaborative Reproducible Research](/images/logos/stat-colloquium.png)
StatTag and StatWrap for Conducting Collaborative Reproducible Research
Leah J. Welty, Northwestern University, Chicago
28.09.2023
10:15 am - 11:45 am
LMU Department of Statistics and via zoom
Reproducible research is challenging in diverse teams with varied tools. This talk introduces StatTag and StatWrap, addressing collaboration hurdles. StatTag integrates Word with statistical code, while StatWrap aids project documentation, promoting reproducible research in diverse teams.
Related
![Link to Privacy, Data Privacy, and Differential Privacy](/images/logos/stat-colloquium.png)
Colloquium • 16.07.2024 • LMU Department of Statistics and via zoom
Privacy, Data Privacy, and Differential Privacy
Colloquium at the LMU Department of Statistics with James Bailie from Harvard University.
![Link to Variational Learning for Large Deep Networks](/images/logos/stat-colloquium.png)
Colloquium • 10.07.2024 • LMU Department of Statistics and via zoom
Variational Learning for Large Deep Networks
Colloquium at the LMU Department of Statistics with Thomas Möllenhoff from RIKEN, Tokyo.
![Link to Can today’s intention to treat have a causal effect on tomorrow’s hazard function?](/images/logos/stat-colloquium.png)
Colloquium • 03.07.2024 • LMU Department of Statistics and via zoom
Can today’s intention to treat have a causal effect on tomorrow’s hazard function?
Colloquium at the LMU Department of Statistics with Jan Beyersmann, University of Ulm.
![Link to The Complexities of Differential Privacy for Survey Data](/images/logos/stat-colloquium.png)
Colloquium • 26.06.2024 • LMU Department of Statistics and via zoom
The Complexities of Differential Privacy for Survey Data
![Link to Resampling-based inference for the average treatment effect in observational studies with competing risks](/images/logos/stat-colloquium.png)
Colloquium • 19.06.2024 • LMU Department of Statistics and via zoom
Resampling-based inference for the average treatment effect in observational studies with competing risks
This talk explores three resampling methods to construct valid confidence intervals and bands for treatment effect estimation in competing risks studies.