Journal
Harvard Data Science Review
ISSN 2644-2353 · The MIT Press
Published here
17 counted works of 17 linked
| Year | Title | Academics | Sources |
|---|---|---|---|
| 2024 | Grappling with uncertainty in forecasting the 2024 U.S. presidential election | Andrew Gelman | CV OA GS |
| 2024 | Hopes and limitations of reproducible statistics and machine learning | Andrew Gelman | CV OA |
| 2024 | Predicting the 2024 Presidential Election | Ryan D. Enos | OA GS |
| 2024 | Rejoinder: We Can Improve the Usability of the Census Noisy Measurements File | Kosuke Imai | CV OA GS |
| 2024 | Towards generalizing inferences from trials to target populations | Melody Huang | GS |
| 2023 | An Instrumental Variable for Non-Ignorable Nonresponse | Shiro Kuriwaki | CV OA GS |
| 2023 | Challenges in adjusting a survey that overrepresents people interested in politics | Andrew Gelman | CV OA GS |
| 2023 | Comment: The Essential Role of Policy Evaluation for the 2020 Census Disclosure Avoidance System | Kosuke Imai; Shiro Kuriwaki | CV OA GS |
| 2023 | Making Differential Privacy Work for Census Data Users | Kosuke Imai | CV OA GS |
| 2022 | Widening Access to Applied Machine Learning with TinyML | Dustin Tingley | CV OA GS |
| 2021 | Building on the Shoulders of Bears: Next Steps in Data Science Education | Dustin Tingley | CV OA GS |
| 2021 | Challenges in Incorporating Exploratory Data Analysis into Statistical Workflow | Andrew Gelman | OA GS |
| 2021 | Designing for interactive exploratory data analysis requires theories of graphical inference (with discussion and rejoinder) | Andrew Gelman | CV OA GS |
| 2020 | An updated dynamic Bayesian forecasting model for the 2020 election | Andrew Gelman | CV OA GS |
| 2020 | Post-Election Interview with Andrew Gelman and G. Elliott Morris | Andrew Gelman | OA GS |
| 2020 | Predicting the 2020 presidential election | Ryan D. Enos | CV OA |
| 2020 | Towards Principled Unskewing: Viewing 2020 Election Polls Through a Corrective Lens from 2016 | Shiro Kuriwaki | CV OA GS |