| The impact of sentiment and attention measures on stock market volatility F Audrino, F Sigrist, D Ballinari International Journal of Forecasting 36 (2), 334-357, 2020 | 346 | 2020 |
| Grabit: Gradient tree-boosted Tobit models for default prediction F Sigrist, C Hirnschall Journal of Banking & Finance 102, 177-192, 2019 | 113 | 2019 |
| Stochastic partial differential equation based modelling of large space–time data sets F Sigrist, HR Künsch, WA Stahel Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2015 | 109 | 2015 |
| A dynamic nonstationary spatio-temporal model for short term prediction of precipitation F Sigrist, HR Künsch, WA Stahel The Annals of Applied Statistics 6 (4), 1452-1477, 2012 | 108 | 2012 |
| Gaussian Process Boosting F Sigrist Journal of Machine Learning Research 23, 1-46, 2022 | 75 | 2022 |
| Gradient and Newton boosting for classification and regression F Sigrist Expert Systems With Applications 167, 114080, 2021 | 72 | 2021 |
| Maximum likelihood estimation of spatially varying coefficient models for large data with an application to real estate price prediction JA Dambon, F Sigrist, R Furrer Spatial Statistics 41, 100470, 2021 | 53 | 2021 |
| Using the censored gamma distribution for modeling fractional response variables with an application to loss given default F Sigrist, WA Stahel ASTIN Bulletin 41 (2), 673-710, 2011 | 44 | 2011 |
| When does attention matter? The effect of investor attention on stock market volatility around news releases D Ballinari, F Audrino, F Sigrist International Review of Financial Analysis 82, 102185, 2022 | 43 | 2022 |
| Latent Gaussian Model Boosting F Sigrist IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (2), 1894-1905, 2023 | 35 | 2023 |
| Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities F Sigrist, N Leuenberger European Journal of Operational Research 305 (3), 1390-1406, 2023 | 31 | 2023 |
| KTBoost: Combined kernel and tree boosting F Sigrist Neural Processing Letters 53 (2), 1147-1160, 2021 | 26 | 2021 |
| spate: An R package for spatio-temporal modeling with a stochastic advection-diffusion process F Sigrist, HR Künsch, WA Stahel Journal of Statistical Software 63 (14), 1-23, 2015 | 25* | 2015 |
| An autoregressive spatio-temporal precipitation model F Sigrist, HR Künsch, WA Stahel Procedia Environmental Sciences 3, 2-7, 2011 | 14 | 2011 |
| Examining the vintage effect in hedonic pricing using spatially varying coefficients models: a case study of single-family houses in the Canton of Zurich JA Dambon, SS Fahrländer, S Karlen, M Lehner, J Schlesinger, F Sigrist, ... Swiss Journal of Economics and Statistics 158 (1), 1-14, 2022 | 12 | 2022 |
| A comparison of machine learning methods for data with high-cardinality categorical variables F Sigrist arXiv preprint arXiv:2307.02071, 2023 | 5 | 2023 |
| varycoef: An R package for Gaussian process-based spatially varying coefficient models JA Dambon, F Sigrist, R Furrer arXiv preprint arXiv:2106.02364, 2021 | 5 | 2021 |
| Joint variable selection of both fixed and random effects for Gaussian process-based spatially varying coefficient models JA Dambon, F Sigrist, R Furrer International Journal of Geographical Information Science 36 (12), 2525-2548, 2022 | 4 | 2022 |
| Deep learning for real estate price prediction L Walthert, F Sigrist Available at SSRN 3393434, 2019 | 4 | 2019 |
| Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models P Kündig, F Sigrist Journal of the American Statistical Association (in press), 2024 | 2 | 2024 |
Hans R. KuenschSeminar fur Statistik, Department of Mathematics, ETH ZurichAdresse e-mail validée de stat.math.ethz.ch