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

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