Papers

Publications

• Wakayama, T. and Sugasawa, S. (2024), “Spatiotemporal Factor Models for Functional Data with Application to Population Map Forecast”. Spatial Statistics. (publication, code)

• Wakayama, T. and Sugasawa, S. (2024), “Functional Horseshoe Smoothing for Functional Trend Estimation”. Statistica Sinica. (publication, arXiv)

• Wakayama, T. and Imaizumi, M. (2024), “Fast Convergence on Perfect Classification for Functional Data”. Statistica Sinica. (publication, arXiv)

• Wakayama, T. and Sugasawa, S. (2023), “Trend Filtering for Functional Data”. Stat. (open access, code)

Preprints

• Wakayama, T., and Banerjee, S. (2024), “Process-based Inference for Spatial Energetics Using Bayesian Predictive Stacking”. arXiv preprint, arXiv:2405.09906, code.

• Wakayama, T. and Sugasawa, S. (2024), “Ensemble Prediction via Covariate-dependent Stacking”. arXiv preprint, arXiv:2408.09755, code.

• Jin, Y., Wakayama, T., Jiang, R., and Sugasawa, S. (2024), “Clustered Factor Analysis for Multivariate Spatial Data”. arXiv preprint, arXiv:2409.07018, code.

• Wakayama, T. (2024), “Bayesian Inference for Consistent Predictions in Overparameterized Nonlinear Regression”. arXiv preprint, arXiv:2404.04498, code.

• Wakayama, T., and Matsui, H. (2023), “Functional Data Regression Reconciles with Excess Bases”. arXiv preprint, arXiv:2308.01724, code.

• Wakayama, T., Sugasawa, S., and Kobayashi, G. (2023), “Similarity-based Random Partition Distribution for Clustering Functional Data”. arXiv preprint, arXiv:2308.01704, code.

• Wakayama, T. and Imaizumi, M. (2023), “Bayesian Analysis for Over-parameterized Linear Model without Sparsity”. arXiv preprint, arXiv:2305.15754.