Papers
Publications
Wakayama, T. and Sugasawa, S. (2024), “Ensemble Prediction via Covariate-dependent Stacking”. Statistics and Computing. (publication). Keywords: ensemble learning; stacking; covariate-dependent weights; oracle inequality; spatio-temporal prediction.
Wakayama, T., and Matsui, H. (2025), “Reconciling Functional Data Regression with Excess Bases”. Behaviormetrika, arXiv:2308.01724. *Keywords: functional data regression; overparameterization; double descent; basis expansion.
Wakayama, T., Sugasawa, S., and Kobayashi, G. (2025+), “Similarity-based Random Partition Distribution for Clustering Functional Data”. Journal of the Royal Statistical Society, Series C. (publication). Keywords: random partition distribution; clustering; similarity information; state-space model; nonparametric Bayes.
Jin, Y., Wakayama, T., Jiang, R., and Sugasawa, S. (2025), “Clustered Factor Analysis for Multivariate Spatial Data”. Spatial Statistics. (publication, code) Keywords: factor analysis; heterogeneity; K-means algorithm; spatial clustering.
Wakayama, T. and Sugasawa, S. (2024), “Spatiotemporal Factor Models for Functional Data with Application to Population Map Forecast”. Spatial Statistics. (publication, code) Keywords: state-space model; spatiotemporal factor model; horseshoe prior; population flow forecasting.
Wakayama, T. and Sugasawa, S. (2024), “Functional Horseshoe Smoothing for Functional Trend Estimation”. Statistica Sinica. (publication, arXiv) Keywords: functional time series; shrinkage prior; locally adaptive smoothing; trend filtering.
Wakayama, T. and Imaizumi, M. (2024), “Fast Convergence on Perfect Classification for Functional Data”. Statistica Sinica. (publication, arXiv) Keywords: perfect classification; RKHS classifier; exponential convergence.
Wakayama, T. and Sugasawa, S. (2023), “Trend Filtering for Functional Data”. Stat. (open access, code) Keywords: ADMM; fused-lasso; grpuped lasso; basis expansion; cross-validation.
Preprints
Wakayama, T., and Banerjee, S. (2024), “Process-based Inference for Spatial Energetics Using Bayesian Predictive Stacking”. arXiv preprint, arXiv:2405.09906, code. Keywords:* spatial energetics; wearable sensors; Bayesian predictive stacking; conjugate process models; mobile health inference.
Wakayama, T. (2024), “Bayesian Inference for Consistent Predictions in Overparameterized Nonlinear Regression”. arXiv preprint, arXiv:2404.04498, code. Keywords: overparameterized nonlinear regression; Bayesian adaptive prior; posterior contraction; single-neuron & GLM; uncertainty quantification.
Wakayama, T. and Imaizumi, M. (2023), “Bayesian Analysis for Over-parameterized Linear Model without Sparsity”. arXiv preprint, arXiv:2305.15754. Keywords: high-dimensional Bayesian regression; spectral prior (eigenvector-aligned); posterior contraction rates; Bernstein–von Mises approximation; non-sparse.