INSEAD the business school for the world

Research Articles


Satopää, V. A., Jensen, S. T., Pemantle, R., and Ungar, L. H. (2017) “Partial Information Framework: Aggregating Estimates from Diverse Information Sources.” The Electronic Journal of Statistics 11: 3781-3814. (Paper)

George, E., Rockova, V., Rosenbaum, P. R., Satopää, V. A., Silber, J. H. (2017) “Mortality Rate Estimation and Standardization for Public Reporting: Medicare’s Hospital Compare.” Journal of the American Statistical Association  112:519: 933-947. (Paper)

Ernst, P., Pemantle, R., Satopää, V. A., and Ungar, L. H. (2016) “Bayesian Aggregation of Two Forecasts in the Partial Information Framework.” Statistics & Probability Letters 119: 170-180. (Paper). 

Silber, J. H., Satopää, V. A., Rockova, V., Wang, W., Hill, A., Even-Shoshan, O., George, E., and Rosenbaum, P. R. (2016) “Improving Medicare’s Hospital Compare Mortality Model.” Health Services Research 51.S2: 1229-1247. (Paper)

Satopää, V. A. (2016) Invited discussion of “Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations and Forecast Rankings” by Werner Ehm, Tilmann Gneiting, Alexander Jordan, and Fabian Krüger. The Journal of the Royal Statistical Society: Series B 78: 534-5. 

Satopää, V. A., Pemantle, R., and Ungar, L. H. (2016) “Modeling Probability Forecasts via Information Diversity.” Journal of the American Statistical Association 111.516: 1623-1633. (Paper, Supplementary Material, Code)

Satopää, V. A., Jensen, S. T., Mellers, B. A., Tetlock, P. E., and Ungar, L. H. (2014). “Probability Aggregation in Time-Series: Dynamic Hierarchical Modeling of Sparse Expert Beliefs.” Annals of Applied Statistics 8.2: 1256-1280.  (Paper)

Satopää, V. A., Baron, J., Foster, D. P., Mellers, B. A., Tetlock, P. E., and Ungar, L. H. (2014). “Combining Multiple Probability Predictions Using a Simple Logit Model.” International Journal of Forecasting 30.2: 344-356. (Paper)

Klingenberg, B. and Satopää, V. A. (2013). “Simultaneous Confidence Intervals for Comparing Margins of Multivariate Binary Data.” Computational Statistics & Data Analysis 64: 87-98. (Paper, Supplementary Material, (Fake) Data Set A and B of AEs, R/C++ Code for Restricted MLE, R/C++ Code for restricted GEE)

Satopää, V. A. and De Veaux, R. D. (2012). “A Robust Boosting Algorithm for Chemical Modeling.” Current Analytical Chemistry 8.2: 254-265. (Paper)

Ungar, L. H., Mellers, B., Satopää, V. A., Tetlock, P. E., and Baron, J. (2012). “The Good Judgment Project: A Large Scale Test of Different Methods of Combining Expert Predictions.” In 2012 AAAI Fall Symposium Series. (Paper)

Satopää, V. A., Albrecht, J., Irwin, D., and Raghavan, B. (2011). “Finding a “Kneedle” in a Haystack: Detecting Knee Points in System Behavior.” In ICDCSW ’11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems Workshops, pp. 166-171. IEEE. (Paper, Code)

Under Review:

Satopää, V. A., Salikhov, M., Tetlock, P., and Mellers, B. "Bias, Information, Noise: The BIN Model of Forecasting" Major revision in Management Science.

Keppo, J., and Satopää, V. A. "Shepherding the Herd” Submitted.

Palley, A., and Satopää, V. A. "Expert Selection Within a Single Judgment Problem" Submitted. (Paper)

Bertani, N., Satopää, V. A., and Jensen S. "Joint Bottom-Up Method for Hierarchical Time-Series: Application to Australian Tourism" Submitted.

Working Papers:

Satopää, V. A. “Collecting Information from Multiple Forecasters: Inefficiency of Central Tendency.” Working paper. (Paper)

Satopää, V. A. “Automatic Bayesian Aggregation of Noisy Probability Predictions.”

Satopää, V. A. “The Super Wisdom of the Crowd,” (targeted for popular press outlets).

Jia, Y., Keppo, J, and Satopää, V. A. "The Wisdom of Herding Crowd"

Other Projects:

Bertani, N., Satopää, V. A., and Jensen S. “Urban Planning of Safer Cities Based on Hierarchical Spatio-Temporal Forecasting of Crime”

Mellers, B., Salikhov, M., Satopää, V. A., and Tetlock, P.  "Bias, Information, and Noise in Forecast Aggregation"