High-dimensional macroeconomic stress testing of corporate recovery rate
Nazemi, A.; Baumann, F.; Schienle, M.; Fabozzi, F. J.
2024. Quantitative Finance, 24 (11), 1669–1678. doi:10.1080/14697688.2024.2414758

Guidance for the Effective Use of Business Intelligence and Analytics Systems. PhD dissertation
Gunklach, J.
2024, October 9. Karlsruher Institut für Technologie (KIT)

Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts
Song, M.; Yang, D.; Lerch, S.; Xia, X.; Yagli, G. M.; Bright, J. M.; Shen, Y.; Liu, B.; Liu, X.; Mayer, M. J.
2024. Advances in Atmospheric Sciences, 41 (7), 1417–1437. doi:10.1007/s00376-023-3184-5

Machine Learning Methods for Postprocessing Ensemble Forecasts of Wind Gusts: Data
Schulz, B.; Lerch, S.
2024, May 28. doi:10.35097/afEBrMYqNrxxvrLX

Data Science-Based Analysis of Special Situations in Corporate Bonds. PhD dissertation
Baumann, F.
2024, April 9. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000169769

Large Spillover Networks of Nonstationary Systems
Chen, S.; Schienle, M.
2024. Journal of Business and Economic Statistics, 42 (2), 422–436. doi:10.1080/07350015.2022.2099870

Generative machine learning methods for multivariate ensemble postprocessing
Chen, J.; Janke, T.; Steinke, F.; Lerch, S.
2024. The Annals of Applied Statistics, 18 (1), 159–183. doi:10.1214/23-AOAS1784

Deep Learning for Postprocessing Global Probabilistic Forecasts on Subseasonal Time Scales
Horat, N.; Lerch, S.
2024. Monthly Weather Review, 152 (3), 667–687. doi:10.1175/MWR-D-23-0150.1

Learning to Forecast: The Probabilistic Time Series Forecasting Challenge
Bracher, J.; Koster, N.; Krüger, F.; Lerch, S.
2024. The American Statistician, 78 (1), 115–127. doi:10.1080/00031305.2023.2199800

Developments in Theory and Application of Copulas: Variance Estimation, Economic Modeling, Forecasting and Machine Learning. PhD dissertation
Kächele, F.
2023, October 10. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000162773

Collaborative nowcasting of COVID-19 hospitalization incidences in Germany
Wolffram, D.; Abbott, S.; an der Heiden, M.; Funk, S.; Günther, F.; Hailer, D.; Heyder, S.; Hotz, T.; van de Kassteele, J.; Küchenhoff, H.; Müller-Hansen, S.; Syliqi, D.; Ullrich, A.; Weigert, M. E.; Schienle, M.; Bracher, J. E.
2023. PLOS Computational Biology, 19 (8), Art.-Nr.: e1011394. doi:10.1371/journal.pcbi.1011394

Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States
Ray, E. L.; Brooks, L. C.; Bien, J.; Biggerstaff, M.; Bosse, N. I.; Bracher, J.; Cramer, E. Y.; Funk, S.; Gerding, A.; Johansson, M. A.; Rumack, A.; Wang, Y.; Zorn, M.; Tibshirani, R. J.; Reich, N. G.
2023. International Journal of Forecasting, 39 (3), 1366–1383. doi:10.1016/j.ijforecast.2022.06.005

Statistical Postprocessing of Numerical Weather Prediction Forecasts using Machine Learning. PhD dissertation
Schulz, B.
2023, May 25. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000158905

Corrigendum: Machine Learning Methods for Postprocessing Ensemble Forecasts of Wind Gusts: A Systematic Comparison
Schulz, B.; Lerch, S.
2023. Monthly Weather Review, 151 (5), 1321–1322. doi:10.1175/MWR-D-23-0010.1

Forecast Modelling and Scenario Hub - Prototype
Schienle, M.; Mädche, A.; Bracher, J.
2023, April 4

Model Diagnostics and Forecast Evaluation for Quantiles
Gneiting, T.; Wolffram, D.; Resin, J.; Kraus, K.; Bracher, J.; Dimitriadis, T.; Hagenmeyer, V.; Jordan, A. I.; Lerch, S.; Phipps, K.; Schienle, M.
2023. Annual Review of Statistics and Its Application, 10. doi:10.1146/annurev-statistics-032921-020240

Probabilistic solar forecasting: Benchmarks, post-processing, verification
Gneiting, T.; Lerch, S.; Schulz, B.
2023. Solar Energy, 252, 72–80. doi:10.1016/j.solener.2022.12.054

Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany
Brockhaus, E. K.; Wolffram, D.; Stadler, T.; Osthege, M.; Mitra, T.; Littek, J. M.; Krymova, E.; Klesen, A. J.; Huisman, J. S.; Heyder, S.; Helleckes, L. M.; an der Heiden, M.; Funk, S.; Abbott, S.; Bracher, J.
2023. PLOS Computational Biology, 19 (11), Art.-Nr.: e1011653. doi:10.1371/journal.pcbi.1011653

Direction Augmentation in the Evaluation of Armed Conflict Predictions
Bracher, J.; Rüter, L.; Krüger, F.; Lerch, S.; Schienle, M.
2023. doi:10.48550/arXiv.2304.12108

Direction Augmentation in the Evaluation of Armed Conflict Predictions
Bracher, J.; Rüter, L.; Krüger, F.; Lerch, S.; Schienle, M.
2023. International Interactions, 49 (6), 989–1004. doi:10.1080/03050629.2023.2255923

The EUPPBench postprocessing benchmark dataset v1.0
Demaeyer, J.; Bhend, J.; Lerch, S.; Primo, C.; Van Schaeybroeck, B.; Atencia, A.; Ben Bouallègue, Z.; Chen, J.; Dabernig, M.; Evans, G.; Faganeli Pucer, J.; Hooper, B.; Horat, N.; Jobst, D.; Merše, J.; Mlakar, P.; Möller, A.; Mestre, O.; Taillardat, M.; Vannitsem, S.
2023. Earth System Science Data, 15 (6), 2635–2653. doi:10.5194/essd-15-2635-2023

Scoring epidemiological forecasts on transformed scales
Bosse, N. I.; Abbott, S.; Cori, A.; van Leeuwen, E.; Bracher, J.; Funk, S.
2023. PLOS Computational Biology, 19 (8), Art.-Nr.: e1011393. doi:10.1371/journal.pcbi.1011393

Can Machine Learning Models be a Suitable Tool for Predicting Central European Cold Winter Weather on Subseasonal to Seasonal Timescales?
Kiefer, S. M.; Lerch, S.; Ludwig, P.; Pinto, J. G.
2023. Artificial Intelligence for the Earth Systems, 2 (4), Art.-Nr.: e230020. doi:10.1175/AIES-D-23-0020.1

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
Sherratt, K.; Gruson, H.; Grah, R.; Johnson, H.; Niehus, R.; Prasse, B.; Sandmann, F.; Deuschel, J.; Wolffram, D.; Abbott, S.; Ullrich, A.; Gibson, G.; Ray, E. L.; Reich, N. G.; Sheldon, D.; Wang, Y.; Wattanachit, N.; Wang, L.; Trnka, J.; Obozinski, G.; Sun, T.; Thanou, D.; Pottier, L.; Krymova, E.; Krymova, E.; Meinke, J. H.; Barbarossa, M. V.; Leithäuser, N.; Mohring, J.; Schneider, J.; Włazło, J.; Fuhrmann, J.; Lange, B.; Rodiah, I.; Baccam, P.; Gurung, H.; Stage, S.; Suchoski, B.; Budzinski, J.; Walraven, R.; Villanueva, I.; Tucek, V.; Smid, M.; Zajíček, M.; Pérez Álvarez, C.; Reina, B.; Bosse, N. I.; Meakin, S. R.; Castro, L.; Fairchild, G.; Michaud, I.; Osthus, D.; Alaimo Di Loro, P.; Maruotti, A.; Eclerová, V.; Kraus, A.; Kraus, D.; Pribylova, L.; Dimitris, B.; Li, M. L.; Saksham, S.; Dehning, J.; Mohr, S.; Priesemann, V.; Redlarski, G.; Bejar, B.; Ardenghi, G.; Parolini, N.; Ziarelli, G.; Bock, W.; Heyder, S.; Hotz, T.; Singh, D. E.; Guzman-Merino, M.; Aznarte, J. L.; Moriña, D.; Alonso, S.; Álvarez, E.; López, D.; Prats, C.; Burgard, J. P.; Rodloff, A.; Zimmermann, T.; Kuhlmann, A.; Zibert, J.; Pennoni, F.; Divino, F.; Català, M.; Lovison, G.; Giudici, P.; Tarantino, B.; Bartolucci, F.; Jona Lasinio, G.; Mingione, M.; Farcomeni, A.; Srivastava, A.; Montero-Manso, P.; Adiga, A.; Hurt, B.; Lewis, B.; Marathe, M.; Porebski, P.; Venkatramanan, S.; Bartczuk, R. P.; Dreger, F.; Gambin, A.; Gogolewski, K.; Gruziel-Słomka, M.; Krupa, B.; Moszyński, A.; Niedzielewski, K.; Nowosielski, J.; Radwan, M.; Rakowski, F.; Semeniuk, M.; Szczurek, E.; Zieliński, J.; Kisielewski, J.; Pabjan, B.; Kirsten, H.; Kheifetz, Y.; Scholz, M.; Biecek, P.; Bodych, M.; Filinski, M.; Idzikowski, R.; Krueger, T.; Ozanski, T.; Ozanski, T.; Bracher, J.
2023. eLife, 12, Art.-Nr.: e81916. doi:10.7554/eLife.81916

Comparison of multivariate post‐processing methods using global ECMWF ensemble forecasts
Lakatos, M.; Lerch, S.; Hemri, S.; Baran, S.
2023. Quarterly Journal of the Royal Meteorological Society, 149 (752), 856–877. doi:10.1002/qj.4436

The United States COVID-19 Forecast Hub dataset
US COVID-19 Forecast Hub Consortium; Cramer, E. Y.; Huang, Y.; Wang, Y.; Ray, E. L.; Cornell, M.; Bracher, J.; Brennen, A.; Rivadeneira, A. J. C.; Gerding, A.; House, K.; Jayawardena, D.; Kanji, A. H.; Khandelwal, A.; Le, K.; Mody, V.; Mody, V.; Niemi, J.; Stark, A.; Shah, A.; Wattanchit, N.; Zorn, M. W.; Reich, N. G.; Gneiting, T.; Mühlemann, A.; Gu, Y.; Chen, Y.; Chintanippu, K.; Jivane, V.; Khurana, A.; Kumar, A.; Lakhani, A.; Mehrotra, P.; Pasumarty, S.; Shrivastav, M.; You, J.; Bannur, N.; Deva, A.; Jain, S.; Kulkarni, M.; Merugu, S.; Raval, A.; Shingi, S.; Tiwari, A.; White, J.; Adiga, A.; Hurt, B.; Lewis, B.; Marathe, M.; Peddireddy, A. S.; Porebski, P.; Venkatramanan, S.; Wang, L.; Dahan, M.; Fox, S.; Gaither, K.; Lachmann, M.; Meyers, L. A.; Scott, J. G.; Tec, M.; Woody, S.; Srivastava, A.; Xu, T.; Cegan, J. C.; Dettwiller, I. D.; England, W. P.; Farthing, M. W.; George, G. E.; Hunter, R. H.; Lafferty, B.; Linkov, I.; Mayo, M. L.; Parno, M. D.; Rowland, M. A.; Trump, B. D.; Chen, S.; Faraone, S. V.; Hess, J.; Morley, C. P.; Salekin, A.; Wang, D.; Zhang-James, Y.; Baer, T. M.; Corsetti, S. M.; Eisenberg, M. C.; Falb, K.; Huang, Y.; Martin, E. T.; McCauley, E.; Myers, R. L.; Schwarz, T.; Gibson, G. C.; Sheldon, D.; Gao, L.; Ma, Y.; Wu, D.; Yu, R.; Jin, X.; Wang, Y.-X.; Yan, X.; Chen, Y.; Guo, L.; Zhao, Y.; Chen, J.; Gu, Q.; Wang, L.; Xu, P.; Zhang, W.; Zou, D.; Chattopadhyay, I.; Huang, Y.; Lu, G.; Pfeiffer, R.; Sumner, T.; Wang, D.; Wang, L.; Zhang, S.; Zou, Z.; Biegel, H.; Lega, J.; Hussain, F.; Khan, Z.; Van Bussel, F.; McConnell, S.; Guertin, S. L.; Hulme-Lowe, C.; Nagraj, V. P.; Turner, S. D.; Bejar, B.; Choirat, C.; Flahault, A.; Krymova, E.; Lee, G.; Manetti, E.; Namigai, K.; Obozinski, G.; Sun, T.; Thanou, D.; Ban, X.; Shi, Y.; Walraven, R.; Hong, Q.-J.; van de Walle, A.; Ben-Nun, M.; Riley, S.; Riley, P.; Turtle, J.; Cao, D.; Galasso, J.; Cho, J. H.; Jo, A.; DesRoches, D.; Forli, P.; Hamory, B.; Koyluoglu, U.; Kyriakides, C.; Leis, H.; Milliken, J.; Moloney, M.; Morgan, J.; Nirgudkar, N.; Ozcan, G.; Piwonka, N.; Ravi, M.; Schrader, C.; Shakhnovich, E.; Siegel, D.; Spatz, R.; Stiefeling, C.; Wilkinson, B.; Wong, A.; Cavany, S.; España, G.; Moore, S.; Oidtman, R.; Perkins, A.; Ivy, J. S.; Mayorga, M. E.; Mele, J.; Rosenstrom, E. T.; Swann, J. L.; Kraus, A.; Kraus, D.; Bian, J.; Cao, W.; Gao, Z.; Ferres, J. L.; Li, C.; Liu, T.-Y.; Xie, X.; Zhang, S.; Zheng, S.; Chinazzi, M.; Vespignani, A.; Xiong, X.; Davis, J. T.; Mu, K.; Piontti, A. P. y; Baek, J.; Farias, V.; Georgescu, A.; Levi, R.; Sinha, D.; Wilde, J.; Zheng, A.; Lami, O. S.; Bennouna, A.; Ndong, D. N.; Perakis, G.; Singhvi, D.; Spantidakis, I.; Thayaparan, L.; Tsiourvas, A.; Weisberg, S.; Jadbabaie, A.; Sarker, A.; Shah, D.; Celi, L. A.; Penna, N. D.; Sundar, S.; Berlin, A.; Gandhi, P. D.; McAndrew, T.; Piriya, M.; Chen, Y.; Hlavacek, W.; Lin, Y. T.; Mallela, A.; Miller, E.; Neumann, J.; Posner, R.; Wolfinger, R.; Castro, L.; Fairchild, G.; Michaud, I.; Osthus, D.; Wolffram, D.; Karlen, D.; Panaggio, M. J.; Kinsey, M.; Mullany, L. C.; Rainwater-Lovett, K.; Shin, L.; Tallaksen, K.; Wilson, S.; Brenner, M.; Coram, M.; Edwards, J. K.; Joshi, K.; Klein, E.; Hulse, J. D.; Grantz, K. H.; Hill, A. L.; Kaminsky, K.; Kaminsky, J.; Keegan, L. T.; Lauer, S. A.; Lee, E. C.; Lemaitre, J. C.; Lessler, J.; Meredith, H. R.; Perez-Saez, J.; Shah, S.; Smith, C. P.; Truelove, S. A.; Wills, J.; Gardner, L.; Marshall, M.; Nixon, K.; Burant, J. C.; Budzinski, J.; Chiang, W.-H.; Mohler, G.; Gao, J.; Glass, L.; Qian, C.; Romberg, J.; Sharma, R.; Spaeder, J.; Sun, J.; Xiao, C.; Gao, L.; Gu, Z.; Kim, M.; Li, X.; Wang, Y.; Wang, G.; Wang, L.; Yu, S.; Jain, C.; Bhatia, S.; Nouvellet, P.; Barber, R.; Gaikedu, E.; Hay, S.; Lim, S.; Murray, C.; Pigott, D.; Reiner, R. C.; Baccam, P.; Gurung, H. L.; Stage, S. A.; Suchoski, B. T.; Fong, C.-Y.; Yeung, D.-Y.; Adhikari, B.; Cui, J.; Prakash, B. A.; Rodríguez, A.; Tabassum, A.; Xie, J.; Asplund, J.; Baxter, A.; Keskinocak, P.; Oruc, B. E.; Serban, N.; Arik, S. O.; Dusenberry, M.; Epshteyn, A.; Kanal, E.; Le, L. T.; Li, C.-L.; Pfister, T.; Sinha, R.; Tsai, T.; Yoder, N.; Yoon, J.; Zhang, L.; Wilson, D.; Belov, A. A.; Chow, C. C.; Gerkin, R. C.; Yogurtcu, O. N.; Ibrahim, M.; Lacroix, T.; Le, M.; Liao, J.; Nickel, M.; Sagun, L.; Abbott, S.; Bosse, N. I.; Funk, S.; Hellewell, J.; Meakin, S. R.; Sherratt, K.; Kalantari, R.; Zhou, M.; Karimzadeh, M.; Lucas, B.; Ngo, T.; Zoraghein, H.; Vahedi, B.; Wang, Z.; Sen Pei; Shaman, J.; Yamana, T. K.; Bertsimas, D.; Li, M. L.; Soni, S.; Bouardi, H. T.; Adee, M.; Ayer, T.; Chhatwal, J.; Dalgic, O. O.; Ladd, M. A.; Linas, B. P.; Mueller, P.; Xiao, J.; Bosch, J.; Wilson, A.; Zimmerman, P.; Wang, Q.; Wang, Y.; Xie, S.; Zeng, D.; Bien, J.; Brooks, L.; Green, A.; Hu, A. J.; Jahja, M.; McDonald, D.; Narasimhan, B.; Politsch, C.; Rajanala, S.; Rumack, A.; Simon, N.; Tibshirani, R. J.; Tibshirani, R.; Ventura, V.; Wasserman, L.; Drake, J. M.; O’Dea, E. B.; Abu-Mostafa, Y.; Bathwal, R.; Chang, N. A.; Chitta, P.; Erickson, A.; Goel, S.; Gowda, J.; Jin, Q.; Jo, H.; Kim, J.; Kulkarni, P.; Lushtak, S. M.; Mann, E.; Popken, M.; Soohoo, C.; Tirumala, K.; Tseng, A.; Varadarajan, V.; Vytheeswaran, J.; Wang, C.; Yeluri, A.; Yurk, D.; Zhang, M.; Zlokapa, A.; Pagano, R.; Jain, C.; Tomar, V.; Ho, L.; Huynh, H.; Tran, Q.; Lopez, V. K.; Walker, J. W.; Slayton, R. B.; Johansson, M. A.; Biggerstaff, M.; Reich, N. G.
2022. Scientific Data, 9 (1), Art.-Nr.: 462. doi:10.1038/s41597-022-01517-w

Assessing the impact of policy and regulation interventions in European sovereign credit risk networks: What worked best?
Buse, R.; Schienle, M.; Urban, J.
2022. Journal of International Economics, 139, Art.-Nr.: 103673. doi:10.1016/j.jinteco.2022.103673

Generative machine learning methods for multivariate ensemble post-processing
Chen, J.; Janke, T.; Steinke, F.; Lerch, S.
2022. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000151932

Analysis of pesticide and persistent organic pollutant residues in German bats
Schanzer, S.; Koch, M.; Kiefer, A.; Jentke, T.; Veith, M.; Bracher, F.; Bracher, J.; Müller, C.
2022. Chemosphere, 305, Art.-Nr.: 135342. doi:10.1016/j.chemosphere.2022.135342

Statistical Model Selection and Prediction for Non-standard Data: Insights and Applications in Economics and Finance. PhD dissertation
Görgen, K.
2022, August 9. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000149413

Collaborative Hubs: Making the Most of Predictive Epidemic Modeling
Reich, N. G.; Lessler, J.; Funk, S.; Viboud, C.; Vespignani, A.; Tibshirani, R. J.; Shea, K.; Schienle, M.; Runge, M. C.; Rosenfeld, R.; Ray, E. L.; Niehus, R.; Johnson, H. C.; Johansson, M. A.; Hochheiser, H.; Gardner, L.; Bracher, J.; Borchering, R. K.; Biggerstaff, M.
2022. American Journal of Public Health, 112 (6), 839–842. doi:10.2105/AJPH.2022.306831

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
Cramer, E. Y.; Ray, E. L.; Lopez, V. K.; Bracher, J.; Brennen, A.; Castro Rivadeneira, A. J.; Gerding, A.; Gneiting, T.; House, K. H.; Huang, Y.; Jayawardena, D.; Kanji, A. H.; Khandelwal, A.; Le, K.; Mühlemann, A.; Niemi, J.; Shah, A.; Stark, A.; Wang, Y.; Wattanachit, N.; Zorn, M. W.; Gu, Y.; Jain, S.; Bannur, N.; Deva, A.; Kulkarni, M.; Merugu, S.; Raval, A.; Shingi, S.; Tiwari, A.; White, J.; Abernethy, N. F.; Woody, S.; Dahan, M.; Fox, S.; Gaither, K.; Lachmann, M.; Meyers, L. A.; Scott, J. G.; Tec, M.; Srivastava, A.; George, G. E.; Cegan, J. C.; Dettwiller, I. D.; England, W. P.; Farthing, M. W.; Hunter, R. H.; Lafferty, B.; Linkov, I.; Mayo, M. L.; Parno, M. D.; Rowland, M. A.; Trump, B. D.; Zhang-James, Y.; Chen, S.; Faraone, S. V.; Hess, J.; Morley, C. P.; Salekin, A.; Wang, D.; Corsetti, S. M.; Baer, T. M.; Eisenberg, M. C.; Falb, K.; Huang, Y.; Martin, E. T.; McCauley, E.; Myers, R. L.; Schwarz, T.; Sheldon, D.; Gibson, G. C.; Yu, R.; Gao, L.; Ma, Y.; Wu, D.; Yan, X.; Jin, X.; Wang, Y.-X.; Chen, Y.; Guo, L.; Zhao, Y.; Gu, Q.; Chen, J.; Wang, L.; Xu, P.; Zhang, W.; Zou, D.; Biegel, H.; Lega, J.; McConnell, S.; Nagraj, V. P.; Guertin, S. L.; Hulme-Lowe, C.; Turner, S. D.; Shi, Y.; Ban, X.; Walraven, R.; Hong, Q.-J.; Kong, S.; van de Walle, A.; Turtle, J. A.; Ben-Nun, M.; Riley, S.; Riley, P.; Koyluoglu, U.; DesRoches, D.; Forli, P.; Hamory, B.; Kyriakides, C.; Leis, H.; Milliken, J.; Moloney, M.; Morgan, J.; Nirgudkar, N.; Ozcan, G.; Piwonka, N.; Ravi, M.; Schrader, C.; Shakhnovich, E.; Siegel, D.; Spatz, R.; Stiefeling, C.; Wilkinson, B.; Wong, A.; Cavany, S.; España, G.; Moore, S.; Oidtman, R.; Perkins, A.; Kraus, D.; Kraus, A.; Gao, Z.; Bian, J.; Cao, W.; Lavista Ferres, J.; Li, C.; Liu, T.-Y.; Xie, X.; Zhang, S.; Zheng, S.; Vespignani, A.; Chinazzi, M.; Davis, J. T.; Mu, K.; Pastore y Piontti, A.; Xiong, X.; Zheng, A.; Baek, J.; Farias, V.; Georgescu, A.; Levi, R.; Sinha, D.; Wilde, J.; Perakis, G.; Bennouna, M. A.; Nze-Ndong, D.; Singhvi, D.; Spantidakis, I.; Thayaparan, L.; Tsiourvas, A.; Sarker, A.; Jadbabaie, A.; Shah, D.; Della Penna, N.; Celi, L. A.; Sundar, S.; Wolfinger, R.; Osthus, D.; Castro, L.; Fairchild, G.; Michaud, I.; Karlen, D.; Kinsey, M.; Mullany, L. C.; Rainwater-Lovett, K.; Shin, L.; Tallaksen, K.; Wilson, S.; Lee, E. C.; Dent, J.; Grantz, K. H.; Hill, A. L.; Kaminsky, J.; Kaminsky, K.; Keegan, L. T.; Lauer, S. A.; Lemaitre, J. C.; Lessler, J.; Meredith, H. R.; Perez-Saez, J.; Shah, S.; Smith, C. P.; Truelove, S. A.; Wills, J.; Marshall, M.; Gardner, L.; Nixon, K.; Burant, J. C.; Wang, L.; Gao, L.; Gu, Z.; Kim, M.; Li, X.; Wang, G.; Wang, Y.; Yu, S.; Reiner, R. C.; Barber, R.; Gakidou, E.; Hay, S. I.; Lim, S.; Murray, C.; Pigott, D.; Gurung, H. L.; Baccam, P.; Stage, S. A.; Suchoski, B. T.; Prakash, B. A.; Adhikari, B.; Cui, J.; Rodríguez, A.; Tabassum, A.; Xie, J.; Keskinocak, P.; Asplund, J.; Baxter, A.; Oruc, B. E.; Serban, N.; Arik, S. O.; Dusenberry, M.; Epshteyn, A.; Kanal, E.; Le, L. T.; Li, C.-L.; Pfister, T.; Sava, D.; Sinha, R.; Tsai, T.; Yoder, N.; Yoon, J.; Zhang, L.; Abbott, S.; Bosse, N. I.; Funk, S.; Hellewell, J.; Meakin, S. R.; Sherratt, K.; Zhou, M.; Kalantari, R.; Yamana, T. K.; Pei, S.; Shaman, J.; Li, M. L.; Bertsimas, D.; Skali Lami, O.; Soni, S.; Tazi Bouardi, H.; Ayer, T.; Adee, M.; Chhatwal, J.; Dalgic, O. O.; Ladd, M. A.; Linas, B. P.; Mueller, P.; Xiao, J.; Wang, Y.; Wang, Q.; Xie, S.; Zeng, D.; Green, A.; Bien, J.; Brooks, L.; Hu, A. J.; Jahja, M.; McDonald, D.; Narasimhan, B.; Politsch, C.; Rajanala, S.; Rumack, A.; Simon, N.; Tibshirani, R. J.; Tibshirani, R.; Ventura, V.; Wasserman, L.; O’Dea, E. B.; Drake, J. M.; Pagano, R.; Tran, Q. T.; Ho, L. S. T.; Huynh, H.; Walker, J. W.; Slayton, R. B.; Johansson, M. A.; Biggerstaff, M.; Reich, N. G.
2022. Proceedings of the National Academy of Sciences of the United States of America, 119 (15), e2113561119. doi:10.1073/pnas.2113561119

Sebastian Funk, Sam Abbott and Johannes Bracher’s discussion contribution to the papers in Session 2 of the Royal Statistical Society’s Special Topic Meeting on Covid‐19 Transmission: 11 June 2021
Funk, S.; Abbott, S.; Bracher, J.
2022. Journal of the Royal Statistical Society: Series A (Statistics in Society), 185 (S1), S103-S104. doi:10.1111/rssa.12974

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021
Bracher, J.; Wolffram, D.; Deuschel, J.; Görgen, K.; Ketterer, J. L.; Ullrich, A.; Abbott, S.; Barbarossa, M. V.; Bertsimas, D.; Bhatia, S.; Bodych, M.; Bosse, N. I.; Burgard, J. P.; Castro, L.; Fairchild, G.; Fiedler, J.; Fuhrmann, J.; Funk, S.; Gambin, A.; Gogolewski, K.; Heyder, S.; Hotz, T.; Kheifetz, Y.; Kirsten, H.; Krueger, T.; Krymova, E.; Leithäuser, N.; Li, M. L.; Meinke, J. H.; Miasojedow, B.; Michaud, I. J.; Mohring, J.; Nouvellet, P.; Nowosielski, J. M.; Ozanski, T.; Radwan, M.; Rakowski, F.; Scholz, M.; Soni, S.; Srivastava, A.; Gneiting, T.; Schienle, M.
2022. Communications Medicine, 2 (1), Art.-Nr.: 136. doi:10.1038/s43856-022-00191-8

Comparing human and model-based forecasts of COVID-19 in Germany and Poland
Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group; Bosse, N. I.; Abbott, S.; Bracher, J.; Hain, H.; Quilty, B. J.; Jit, M.; van Leeuwen, E.; Cori, A.; Funk, S.
2022. (J. M. McCaw, Ed.) PLOS Computational Biology, 18 (9), Art.Nr. e1010405. doi:10.1371/journal.pcbi.1010405

Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing
Silini, R.; Lerch, S.; Mastrantonas, N.; Kantz, H.; Barreiro, M.; Masoller, C.
2022. Earth System Dynamics, 13 (3), 1157–1165. doi:10.5194/esd-13-1157-2022

Evaluating ensemble post‐processing for wind power forecasts
Phipps, K.; Lerch, S.; Andersson, M.; Mikut, R.; Hagenmeyer, V.; Ludwig, N.
2022. Wind Energy, 25 (8), 1379–1405. doi:10.1002/we.2736

Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning
Chapman, W. E.; Delle Monache, L.; Alessandrini, S.; Subramanian, A. C.; Ralph, F. M.; Xie, S.-P.; Lerch, S.; Hayatbini, N.
2022. Monthly Weather Review, 150 (1), 215–234. doi:10.1175/MWR-D-21-0106.1

Machine Learning Methods for Postprocessing Ensemble Forecasts of Wind Gusts: A Systematic Comparison
Schulz, B.; Lerch, S.
2022. Monthly weather review, 150 (1), 235–237. doi:10.1175/MWR-D-21-0150.1

A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave
Bracher, J.; Wolffram, D.; Deuschel, J.; Görgen, K.; Ketterer, J. L.; Ullrich, A.; Abbott, S.; Barbarossa, M. V.; Bertsimas, D.; Bhatia, S.; Bodych, M.; Bosse, N. I.; Burgard, J. P.; Castro, L.; Fairchild, G.; Fuhrmann, J.; Funk, S.; Gogolewski, K.; Gu, Q.; Heyder, S.; Hotz, T.; Kheifetz, Y.; Kirsten, H.; Krueger, T.; Krymova, E.; Li, M. L.; Meinke, J. H.; Michaud, I. J.; Niedzielewski, K.; Ożański, T.; Rakowski, F.; Scholz, M.; Soni, S.; Srivastava, A.; Zieliński, J.; Zou, D.; Gneiting, T.; Schienle, M.
2021. (List of Contributors by Team, Ed.) Nature communications, 12 (1), 5173. doi:10.1038/s41467-021-25207-0

Evaluating epidemic forecasts in an interval format
Bracher, J.; Ray, E. L.; Gneiting, T.; Reich, N. G.
2021. (V. E. Pitzer, Ed.) PLoS Computational Biology, 17 (2), Art.Nr. e1008618. doi:10.1371/JOURNAL.PCBI.1008618

Statistical postprocessing for weather forecasts review, challenges, and avenues in a big data world
Vannitsem, S.; Bremnes, J. B.; Demaeyer, J.; Evans, G. R.; Flowerdew, J.; Hemri, S.; Lerch, S.; Roberts, N.; Theis, S.; Atencia, A.; Bouallègue, Z. B.; Bhend, J.; Dabernig, M.; Cruz, L. de; Hieta, L.; Mestre, O.; Moret, L.; Plenković, I. O.; Schmeits, M.; Taillardat, M.; Bergh, J. van den; Schaeybroeck, B. van; Whan, K.; Ylhaisi, J.
2021. Bulletin of the American Meteorological Society, 102 (3), E681-E699. doi:10.1175/BAMS-D-19-0308.1

Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting
Schulz, B.; El Ayari, M.; Lerch, S.; Baran, S.
2021. Solar Energy, 220, 1016–1031. doi:10.1016/j.solener.2021.03.023

Machine learning for total cloud cover prediction
Baran, Á.; Lerch, S.; El Ayari, M.; Baran, S.
2021. Neural computing & applications, 33, 2605–2620. doi:10.1007/s00521-020-05139-4

Preface: Advances in post-processing and blending of deterministic and ensemble forecasts
Hemri, S.; Lerch, S.; Taillardat, M.; Vannitsem, S.; Wilks, D. S.
2020. Nonlinear processes in geophysics, 27 (4), 519–521. doi:10.5194/npg-27-519-2020

Predictive Inference Based on Markov Chain Monte Carlo Output
Krüger, F.; Lerch, S.; Thorarinsdottir, T.; Gneiting, T.
2020

Predictive Inference Based on Markov Chain Monte Carlo Output
Krüger, F.; Lerch, S.; Thorarinsdottir, T.; Gneiting, T.
2020. International statistical review, 89 (2), 274–301. doi:10.1111/insr.12405

Simulation-based comparison of multivariate ensemble post-processing methods
Lerch, S.; Baran, S.; Möller, A.; Groß, J.; Schefzik, R.; Hemri, S.; Graeter, M.
2020. Nonlinear processes in geophysics, 27 (2), 349–371. doi:10.5194/npg-27-349-2020

Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression
Lang, M. N.; Lerch, S.; Mayr, G. J.; Simon, T.; Stauffer, R.; Zeileis, A.
2020. Nonlinear processes in geophysics, 27 (1), 23–34. doi:10.5194/npg-27-23-2020

A retrospective assessment of different endodontic treatment protocols
Bartols, A.; Bormann, C.; Werner, L.; Schienle, M.; Walther, W.; Dörfer, C. E.
2020. PeerJ, 8, e8495. doi:10.7717/peerj.8495

Detecting Structural Differences in Tail Dependence of Financial Time Series
Bormann, C.; Schienle, M.
2020. Journal of business & economic statistics, 38 (2), 380–392. doi:10.1080/07350015.2018.1506343

Determination of vector error correction models in high dimensions
Liang, C.; Schienle, M.
2019. Journal of econometrics, 208 (2), 418–441. doi:10.1016/j.jeconom.2018.09.018

Evaluating Probabilistic Forecasts with
Jordan, A.; Krüger, F.; Lerch, S.
2019. Journal of statistical software, 90 (12), 1–37. doi:10.18637/jss.v090.i12

Effectiveness of policy and regulation in European sovereign credit risk markets - A network analysis
Buse, R.; Schienle, M.; Urban, J.
2019. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000092476

Determination of vector error correction models in high dimensions
Liang, C.; Schienle, M.
2019. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000092474

Measuring connectedness of euro area sovereign risk
Buse, R.; Schienle, M.
2019. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000092470

Detecting structural differences in tail dependence of financial time series
Bormann, C.; Schienle, M.
2019. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000092468

Measuring Dynamic Spillovers in Networks: Sovereign Contagion, Regulation Effects and Market Sectors. PhD dissertation
Buse, R.
2019. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000091930

Testing for an omitted multiplicative long-term component in GARCH models
Conrad, C.; Schienle, M.
2019. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000090371

Testing for an Omitted Multiplicative Long-Term Component in GARCH Models
Conrad, C.; Schienle, M.
2019. Journal of business & economic statistics, 38 (2), 229–242. doi:10.1080/07350015.2018.1482759

Measuring connectedness of euro area sovereign risk
Buse, R.; Schienle, M.
2019. International journal of forecasting, 35 (1), 25–44. doi:10.1016/j.ijforecast.2018.07.010

Neural Networks for Postprocessing Ensemble Weather Forecasts
Rasp, S.; Lerch, S.
2018. Monthly weather review, 146 (11), 3885–3900. doi:10.1175/MWR-D-18-0187.1

Combining predictive distributions for the statistical post-processing of ensemble forecasts
Baran, S.; Lerch, S.
2018. International journal of forecasting, 34 (3), 477–496. doi:10.1016/j.ijforecast.2018.01.005

High Dimensional Time Series — New Techniques and Applications. PhD dissertation
Liang, C.
2018. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000084976

Credit risk contagion and arbitrage: Evidence from sovereign bond and credit default swap markets. PhD dissertation
Urban, J.
2017. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000073269

Forecaster’s dilemma: Extreme events and forecast evaluation
Lerch, S.; Thorarinsdottir, T. L.; Ravazzolo, F.; Gneiting, T.
2017. Statistical science, 32 (1), 106–127. doi:10.1214/16-STS588

Similarity-based semilocal estimation of post-processing models
Lerch, S.; Baran, S.
2017. Journal of the Royal Statistical Society / C, 66 (1), 29–51. doi:10.1111/rssc.12153

Multivariate Extremes in Financial Markets: New Statistical Testing Methods and Applications. PhD dissertation
Bormann, C.
2017. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000065825

Mixture EMOS model for calibrating ensemble forecasts of wind speed
Baran, S.; Lerch, S.
2016. Environmetrics, 27 (2), 116–130. doi:10.1002/env.2380

Probabilistic forecasting and comparative model assessment, with focus on extreme events. PhD dissertation
Lerch, S.
2016. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000055628

Semiparametric estimation with generated covariates
Mammen, E.; Rothe, C.; Schienle, M.
2016. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000051816

Beyond dimension two: A test for higher-order tail risk
Bormann, C.; Schaumburg, J.; Schienle, M.
2016. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000051814

Systemic risk spillovers in the European banking and sovereign network
Betz, F.; Hautsch, N.; Peltonen, T. A.; Schienle, M.
2016. Journal of financial stability, 25, 206–224. doi:10.1016/j.jfs.2015.10.006

Systemic risk spillovers in the European banking and sovereign network
Betz, F.; Hautsch, N.; Peltonen, T. A.; Schienle, M.
2016. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000051810

Semiparametric Estimation with Generated Covariates
Mammen, E.; Rothe, C.; Schienle, M.
2016. Econometric theory, 32 (5), 1140–1177. doi:10.1017/S0266466615000134

Measuring Connectedness of Euro Area Sovereign Risk
Gätjen, R.; Schienle, M.
2015. Humboldt-Universität zu Berlin

Misspecification Testing in GARCH-MIDAS Models
Conrad, C.; Schienle, M.
2015. Universität Heidelberg

Determination of Vector Error Correction Models in Higher Dimenensions
Liang, C.; Schienle, M.
2015. 11th World Congress of the Econometric Society, Montréal, Canada, Aug 17 - 21, 2015

Beyond Dimension two: A Test for Higher-Order Tail Risk
Bormann, C.; Schaumburg, J.; Schienle, M.
2015. Journal of financial econometrics, 14 (3), 552–580. doi:10.1093/jjfinec/nbv022

Determination of vector error correction models in higher dimensions
Liang, C.; Schienle, M.
2015. 9th International Conference on Computational and Financial Econometrics (CFE), London, UK, 12-14 December, 2015

Financial Network Systemic Risk Contributions
Hautsch, N.; Schaumburg, J.; Schienle, M.
2015. Review of finance, 19 (2), 685–738. doi:10.1093/rof/rfu010

Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes
Hautsch, N.; Malec, P.; Schienle, M.
2014. Journal of financial econometrics, 12 (1), 89–121. doi:10.1093/jjfinec/nbt002

Systemic Risk Spillovers in the European Banking and Sovereign Network
Betz, F.; Hautsch, N.; Peltonen, T.; Schienle, M.
2014. Univ.-Bibliothek Frankfurt am Main. doi:10.2139/ssrn.2504400

Beyond dimension two: A test for higher-order tail risk
Bormann, C.; Schienle, M.; Schaumburg, J.
2014

Additive Models: Extensions and Related Models
Mammen, E.; Park, B. U.; Schienle, M.
2014. The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics. Ed.: J.Racine, Oxford Univ. Press. doi:10.1093/oxfordhb/9780199857944.013.007

Forecasting systemic impact in financial networks
Hautsch, N.; Schaumburg, J.; Schienle, M.
2014. International Journal of Forecasting, 30 (3), 781–794. doi:10.1016/j.ijforecast.2013.09.004

Nonparametric Kernel Density Estimation Near the Boundary
Malec, P.; Schienle, M.
2014. Computational Statistics and Data Analysis, 72, 57–76. doi:10.1016/j.csda.2013.10.023

Forecasting systemic impact in financial networks
Hautsch, N.; Schaumburg, J.; Schienle, M.
2013. Humboldt-Universität zu Berlin

Financial Network Systemic Risk Contributions
Hautsch, N.; Schaumburg, J.; Schienle, M.
2013. Center for Financial Studies (CFS)

Nonparametric regression with nonparametrically generated covariates
Mammen, E.; Rothe, C.; Schienle, M.
2012. The annals of statistics, 40 (2), 1132–1170. doi:10.1214/12-AOS995

Additive Models: Extensions and Related Models
Mammen, E.; Park, B. U.; Schienle, M.
2012. Humboldt-Universität zu Berlin

Nonparametric Kernel Density Estimation Near the Boundary
Malec, P.; Schienle, M.
2012. Humboldt-Universität zu Berlin

Financial Network Systemic Risk Contributions
Hautsch, N.; Schaumburg, J.; Schienle, M.
2012. Humboldt-Universität zu Berlin

Nonparametric Nonstationary Regression with Many Covariates
Schienle, M.
2011. Humboldt-Universität zu Berlin

Semiparametric Estimation with Generated Covariates
Mammen, E.; Rothe, C.; Schienle, M.
2011. Humboldt-Universität zu Berlin