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Ville Satopää
Ville Satopää
Assistant Professor, INSEAD
Verified email at insead.edu - Homepage
Title
Cited by
Cited by
Year
Finding a" kneedle" in a haystack: Detecting knee points in system behavior
V Satopaa, J Albrecht, D Irwin, B Raghavan
2011 31st international conference on distributed computing systems …, 2011
6152011
Combining multiple probability predictions using a simple logit model
VA Satopää, J Baron, DP Foster, BA Mellers, PE Tetlock, LH Ungar
International Journal of Forecasting 30 (2), 344-356, 2014
1152014
The good judgment project: A large scale test of different methods of combining expert predictions
L Ungar, B Mellers, V Satopää, P Tetlock, J Baron
2012 AAAI Fall Symposium Series, 2012
1012012
Modeling probability forecasts via information diversity
VA Satopää, R Pemantle, LH Ungar
Journal of the American Statistical Association 111 (516), 1623-1633, 2016
452016
Probability aggregation in time-series: Dynamic hierarchical modeling of sparse expert beliefs
VA Satopää, ST Jensen, BA Mellers, PE Tetlock, LH Ungar
The Annals of Applied Statistics 8 (2), 1256-1280, 2014
322014
Mortality rate estimation and standardization for public reporting: Medicare’s hospital compare
EI George, V Ročková, PR Rosenbaum, VA Satopää, JH Silber
Journal of the American Statistical Association 112 (519), 933-947, 2017
272017
Improving Medicare's hospital compare mortality model
JH Silber, VA Satopää, N Mukherjee, V Rockova, W Wang, AS Hill, ...
Health services research 51, 1229-1247, 2016
262016
Bias, information, noise: The BIN model of forecasting
VA Satopää, M Salikhov, PE Tetlock, B Mellers
Management Science 67 (12), 7599-7618, 2021
202021
Boosting the wisdom of crowds within a single judgment problem: Weighted averaging based on peer predictions
A Palley, V Satopää
Available at SSRN 3504286, 2022
15*2022
Partial information framework: Model-based aggregation of estimates from diverse information sources
VA Satopää, ST Jensen, R Pemantle, LH Ungar
Electronic Journal of Statistics 11 (2), 3781-3814, 2017
152017
Combining information from multiple forecasters: Inefficiency of central tendency
VA Satopää
arXiv preprint arXiv:1706.06006, 2017
112017
Bayesian aggregation of two forecasts in the partial information framework
P Ernst, R Pemantle, V Satopää, L Ungar
Statistics & Probability Letters 119, 170-180, 2016
92016
Combining and extremizing real-valued forecasts
V Satopää, L Ungar
arXiv preprint arXiv:1506.06405, 2015
62015
Simultaneous confidence intervals for comparing margins of multivariate binary data
B Klingenberg, V Satopää
Computational Statistics & Data Analysis 64, 87-98, 2013
62013
Regularized aggregation of one-off probability predictions
VA Satopää
Operations Research, 2022
42022
Improving the wisdom of crowds with analysis of variance of predictions of related outcomes
VA Satopää
International Journal of Forecasting 37 (4), 1728-1747, 2021
42021
Joint Bottom-Up Method for Forecasting Grouped Time Series: Application to Australian Domestic Tourism
N Bertani, V Satopää, S Jensen
Available at SSRN 3542278, 2021
4*2021
Decomposing the effects of crowd-wisdom aggregators: The bias–information–noise (BIN) model
VA Satopää, M Salikhov, PE Tetlock, B Mellers
International Journal of Forecasting, 2022
32022
Skew-Adjusted Extremized-Mean: A Simple Method for Identifying and Learning From Contrarian Minorities in Groups of Forecasters
B Powell, V Satopää, NJ MacKay, P Tetlock
Available at SSRN, 2022
12022
Herding in Probabilistic Forecasts
Y Jia, J Keppo, V Satopää
Management Science, 2022
2022
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