David Randahl
Visiting researcher at Department of Peace and Conflict Research
- E-mail:
- david.randahl@pcr.uu.se
- Visiting address:
- Gamla Torget 3, 1tr
753 20 Uppsala - Postal address:
- Box 514
751 20 UPPSALA
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Short presentation
PhD Candidate at the Department of Peace and Conflict Research within the Violence Early Warning System (ViEWS) project. The focus of my dissertation project is the application of Machine Learning techniques in predicting one sided violence against civilians. My research interests also include the causes and effects of terrorism, missing data analysis, and other methodological challenges.
Publications
Recent publications
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Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict
Part of Political Analysis, p. 96-108, 2026
- DOI for Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict
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This is not normal!: (Re-) Evaluating the lower n guidelines for regression analysis
Part of Teaching Statistics, p. 130-137, 2026
- DOI for This is not normal!: (Re-) Evaluating the lower n guidelines for regression analysis
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Forecasting electoral violence
Part of International Journal of Forecasting, p. 602-615, 2026
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Part of Journal of Peace Research, p. 2070-2087, 2025
- DOI for The 2023/24 VIEWS Prediction challenge: Predicting the number of fatalities in armed conflict, with uncertainty
- Download full text (pdf) of The 2023/24 VIEWS Prediction challenge: Predicting the number of fatalities in armed conflict, with uncertainty
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Could vote buying be socially desirable?: Exploratory analyses of a ‘failed’ list experiment
Part of Quality and quantity, p. 2337-2355, 2024
- DOI for Could vote buying be socially desirable?: Exploratory analyses of a ‘failed’ list experiment
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All publications
Articles in journal
-
Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict
Part of Political Analysis, p. 96-108, 2026
- DOI for Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict
- Download full text (pdf) of Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict
-
This is not normal!: (Re-) Evaluating the lower n guidelines for regression analysis
Part of Teaching Statistics, p. 130-137, 2026
- DOI for This is not normal!: (Re-) Evaluating the lower n guidelines for regression analysis
- Download full text (pdf) of This is not normal!: (Re-) Evaluating the lower n guidelines for regression analysis
-
Forecasting electoral violence
Part of International Journal of Forecasting, p. 602-615, 2026
-
Part of Journal of Peace Research, p. 2070-2087, 2025
- DOI for The 2023/24 VIEWS Prediction challenge: Predicting the number of fatalities in armed conflict, with uncertainty
- Download full text (pdf) of The 2023/24 VIEWS Prediction challenge: Predicting the number of fatalities in armed conflict, with uncertainty
-
Could vote buying be socially desirable?: Exploratory analyses of a ‘failed’ list experiment
Part of Quality and quantity, p. 2337-2355, 2024
- DOI for Could vote buying be socially desirable?: Exploratory analyses of a ‘failed’ list experiment
- Download full text (pdf) of Could vote buying be socially desirable?: Exploratory analyses of a ‘failed’ list experiment
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Inference with extremes: Accounting for Extreme Values in Count Regression Models
Part of International Studies Quarterly, 2024
- DOI for Inference with extremes: Accounting for Extreme Values in Count Regression Models
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Predicting Escalating and De-Escalating Violence in Africa Using Markov Models
Part of International Interactions, p. 597-613, 2022
- DOI for Predicting Escalating and De-Escalating Violence in Africa Using Markov Models
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ViEWS(2020): Revising and evaluating the ViEWS political Violence Early-Warning System
Part of Journal of Peace Research, p. 599-611, 2021
- DOI for ViEWS(2020): Revising and evaluating the ViEWS political Violence Early-Warning System
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ViEWS: A political violence early-warning system
Part of Journal of Peace Research, p. 155-174, 2019
- DOI for ViEWS: A political violence early-warning system
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Part of Terrorism and Political Violence, p. 373-383, 2018