Fabio Sigrist

I am a Senior Scientist at the Seminar for Statistics at ETH Zurich.

Contact Information

Fabio Sigrist
Seminar for Statistics
ETH Zurich, HG G 24.2
Rämistrasse 101
8092 Zurich, Switzerland
Email: fabio (dot) sigrist (at) stat (dot) math (dot) ethz (dot) ch

Publications

See Google Scholar

Selected publications

• Sigrist, F., Künsch, H. R., & Stahel, W. A. (2015). Stochastic partial differential equation based modelling of large space–time data sets. Journal of the Royal Statistical Society Series B: Statistical Methodology
• Audrino, F., Sigrist, F., & Ballinari, D. (2020). The impact of sentiment and attention measures on stock market volatility. International Journal of Forecasting
• Sigrist, F. (2021). Gradient and Newton boosting for classification and regression. Expert Systems With Applications
• Sigrist, F. (2022). Gaussian process boosting. Journal of Machine Learning Research
• Sigrist, F. (2023). Latent Gaussian model boosting. IEEE Transactions on Pattern Analysis and Machine Intelligence
• Sigrist, F., & Leuenberger, N. (2023). Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities. European Journal of Operational Research
• Kündig, P., & Sigrist, F. (2024+). Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models. Journal of the American Statistical Association (to appear)

Software

See GitHub

Selected software

• GPBoost
  • C++ library with Python and R interfaces for tree-boosting, mixed effects models, and Gaussian processes
  • 500+ stars on GitHub