KEYNOTE TALKS (GMT+2)
Keynote talk 2 | Friday 26.8.2022 | 15:00 - 16:00 | Room: Aula 3 |
New graphical displays for classification | |||
Speaker: P. Rousseeuw Co-authors: J. Raymaekers | Chair: Ana Colubi | ||
Keynote talk 1 | Saturday 27.8.2022 | 13:55 - 14:45 | Room: Aula 3 |
A most surprising but useful result in semi-supervised learning (virtual) | |||
Speaker: G. McLachlan Co-authors: D. Ahfock | Chair: Maria Brigida Ferraro | ||
Keynote talk 3 | Sunday 28.8.2022 | 11:35 - 12:30 | Room: Aula 3 |
Distributed estimation through parallel approximants | |||
Speaker: P. Wolfe Co-authors: A. Chakravorty, W.S. Cleveland | Chair: Erricos Kontoghiorghes |
PARALLEL SESSIONS (GMT+2)
Parallel session B: SDS2022 | Friday 26.8.2022 | 16:30 - 18:10 |
Session SO012 | Room: Aula 3 |
Recent advances in dimension reduction and related methods | Friday 26.8.2022 16:30 - 18:10 |
Chair: Yuichi Mori | Organizer: Yuichi Mori, Hiroshi Yadohisa |
A0176: M. van de Velden, A. Iodice D Enza, A. Markos | |
Biplots in dimension reduction and clustering | |
A0182: K. Tanioka, H. Yadohisa | |
Asymmetric MDS with sparse regularization term | |
A0187: Y. Terada, M. Yamamoto | |
Regularized functional subspace clustering | |
A0153: W. Zhou, L. Zhang, H. Wang | |
Estimation and inference of A heteroskedasticity model with latent semiparametric factors for panel data analysis |
Session SO015 | Room: Aula 4 |
Bayesian learning | Friday 26.8.2022 16:30 - 18:10 |
Chair: Igor Pruenster | Organizer: Igor Pruenster |
A0181: P.G. Bissiri | |
Nonparametric Bayesian modeling and estimation of spatial correlation functions for global data | |
A0175: F. Ascolani | |
Trees of random probability measures and Bayesian nonparametric modelling | |
A0185: A. Canale, M. Guindani, L. D Angelo | |
Bayesian nonparametric analysis of calcium imaging data | |
A0177: T. Rigon, A. Zito, D. Dunson | |
A conjugate prior for the Dirichlet process precision parameter |
Parallel session C: SDS2022 | Saturday 27.8.2022 | 09:00 - 10:15 |
Session SO008 | Room: Aula 3 |
Statistical learning for network data with applications | Saturday 27.8.2022 09:00 - 10:15 |
Chair: Yousri Slaoui | Organizer: Yousri Slaoui |
A0172: A. Ounajim, Y. Slaoui, P.-Y. Louis, D. Frasca, P. Rigoard | |
Mixture of longitudinal factor analysis for modelling heterogeneous longitudinal multivariate data | |
A0204: L. Grill, Y. Slaoui, D. Nortershauser, S. Le Masson | |
Usage of Bayesian neural network in deep reinforcement learning | |
A0202: Y. Slaoui | |
Extension of the stochastic block model to handle networks with weighted nodes with application to EEG data | |
A0203: S. El Adlouni | |
Multivariate extremes for risk assessment |
Session SO031 | Room: Aula 4 |
Models for the analysis and classification of heterogeneous data | Saturday 27.8.2022 09:00 - 10:15 |
Chair: Geoffrey McLachlan | Organizer: Geoffrey McLachlan |
A0163: M. Berrettini, G. Galimberti, S. Ranciati | |
Semiparametric finite mixture of regression models with Bayesian P-splines | |
A0191: D. Ahfock, G. McLachlan | |
An EM algorithm for semi-supervised learning with data augmentation | |
A0192: H. Nguyen | |
(SAM)$^2$: A family of sequential sample averaging algorithms via majorization--minimization |
Parallel session D: SDS2022 | Saturday 27.8.2022 | 10:45 - 12:25 |
Session SO010 | Room: Aula 3 |
Text based indicators in economics and finance | Saturday 27.8.2022 10:45 - 12:25 |
Chair: Peter Winker | Organizer: Maria Brigida Ferraro, Peter Winker |
A0180: C. Funk, E. Toenjes, L. Breuer, R. Teuber | |
Difference in SDG reporting of research articles using zero-shot text classification | |
A0196: I. Cozzolino, M.B. Ferraro, P. Winker | |
A novel fuzzy spectral clustering approach for text data | |
A0156: S. Makarova, W. Charemza, K. Rybinski | |
Anti-pandemic restrictions, uncertainty and sentiment in seven countries | |
A0171: P. Winker, D. Lenz, A. Latifi | |
Text based innovation indicators a progress report |
Session SO023 | Room: Aula 4 |
Computational and methodological challenges in environmental data | Saturday 27.8.2022 10:45 - 12:25 |
Chair: Abhi Datta | Organizer: Stefano Castruccio |
A0152: J. Park, I.H. Jin | |
Bayesian model selection for ultrahigh dimensional doubly intractable distributions | |
A0160: W. Chang, J. Wang, S. Bhatnagar, S. Kim | |
Computer model calibration with time series data using deep learning and quantile regression | |
A0162: B. Sanso, X. Zheng, A. Kottas | |
Nearest neighbors processes for non-Gaussian geostatistical data | |
A0184: L. Ippoliti, A. Ferretti, R. Bhansali, P. Valentini | |
Long memory random fields on regular lattices |
Parallel session F: SDS2022 | Saturday 27.8.2022 | 14:55 - 16:35 |
Session SO021 | Room: Aula 3 |
Analytical challenges with complex data analysis | Saturday 27.8.2022 14:55 - 16:35 |
Chair: Anna Gottard | Organizer: Taps Maiti |
A0161: S. Chatterjee | |
Bayesian inference in large complex networks | |
A0198: A. Montanari, L. Anderlucci | |
Perturbing data to address dataset shift in supervised classification | |
A0205: X. Bi, X. Shen | |
Distribution-invariant differential privacy | |
A0206: A. Gottard, A. Panzera | |
Bayesian networks for dihedral angles |
Session SO033 | Room: Aula 4 |
Nonasymptotic statistics and econometric | Saturday 27.8.2022 14:55 - 16:35 |
Chair: Zhao Ren | Organizer: Zhao Ren |
A0168: Z. Ren | |
Heteroskedastic sparse PCA in high dimensions | |
A0170: W. Wang | |
$L^2$ inference of change-points for high-dimensional time series | |
A0173: R. Chen | |
Kronecker product approximation for matrix approximation, denoising and completion | |
A0174: W. Zhou, K.M. Tan, X. He | |
Robust estimation and inference for expected shortfall regression |
Parallel session G: SDS2022 | Saturday 27.8.2022 | 17:05 - 18:45 |
Session SO006 | Room: Aula 3 |
Functional and object data analysis | Saturday 27.8.2022 17:05 - 18:45 |
Chair: Xiaoke Zhang | Organizer: Jane-Ling Wang |
A0166: P. Dubey, H.-G. Mueller | |
Modeling time-varying random objects and dynamic networks | |
A0167: S. Golovkine, V. Patilea, N. Klutchnikoff | |
Learning the regularity of curves in functional data analysis and applications | |
A0164: C. Zhu, H.-G. Mueller | |
Spherical autoregressive models, with application to distributional and compositional time series | |
A0159: X. Zhang, Z. Qi, R. Miao | |
Proximal learning for individualized treatment regimes under unmeasured confounding |
Session SO019 | Room: Aula 4 |
Machine learning for spatial analysis | Saturday 27.8.2022 17:05 - 18:45 |
Chair: Abhi Datta | Organizer: Bertrand Clarke |
A0183: A. Datta | |
Generalizing random forests for spatially correlated data | |
A0190: R. Guhaniyogi | |
Data Compression and Distributed Inference in Spatial Analysis | |
A0208: A. Jaeger | |
Classification of ENSO phases using topological data analysis | |
A0201: D. Hammerling, H. Huang, L. Blake, M. Katzfuss | |
Nonstationary spatial modeling of massive global satellite data |
Parallel session H: SDS2022 | Sunday 28.8.2022 | 09:00 - 11:05 |
Session SO004 | Room: Aula 4 |
Statistical data science (virtual) | Sunday 28.8.2022 09:00 - 11:05 |
Chair: Maria Brigida Ferraro | Organizer: Maria Brigida Ferraro |
A0189: Z. Li | |
Change point inference for high-dimensional correlation matrix | |
A0194: D. Chakrabarty, N. Paul | |
Estimating specification parameters while learning unknowns, in a misspecified model | |
A0169: M. Zafer Merhi, D. Lin, A. Essaghir, Z. Shkedy | |
Identification of antigen specificity in single cell RNAseq experiments using biclustering methods for binary data | |
A0188: S. Chen | |
Machine learning based mass imputation approaches for combining probability sample and nonprobability sample | |
A0158: M.A. Kaygusuz, V. Purutcuoglu | |
Effect of bootstrapping in sparse biological data and model selection via likelihood ratio test |
Session SC014 | Room: Aula 3 |
Statistical data science | Sunday 28.8.2022 09:00 - 11:05 |
Chair: Ori Davidov | Organizer: CMStatistics |
A0178: M. Felix, D. La Vecchia | |
Semiparametric estimation for time series: A frequency domain approach based on optimal transportation theory | |
A0179: H. Fallahgoul, L. Mancini | |
Portfolio choice when stock returns may disappoint: An empirical analysis based on L-moments | |
A0193: F. Setoudehtazangi, M. Caporin | |
Mixtures, heavy tails, asymmetry and conditional heteroskedasticity in financial returns | |
A0199: S. Jokubaitis, D. Celov | |
Business cycle synchronization in the EU: A regional-sectoral look through soft clustering and wavelet decomposition | |
A0165: O. Davidov | |
Graphical linear models for paired comparison data |