Methods Center

Kento Okyuama, M.Sc.

Research Scientist at the Methods Center

Portrait Kento Okuyama

Office
Methods Center
Haußerstr. 11
72076 Tübingen

kento.okuyamaspam prevention@uni-tuebingen.de

Key Research Topics

  • Dynamic latent variable modeling
  • Forecasting in intensive longitudinal data (e.g., filtering methods)
  • Clustering/latent class analysis, regime-switching state-space modeling
  • Multi-level modeling (separation of intra- and inter-individual effects)

Profile

since 2024
Research Scientist

Methods Center, University of Tübingen

2021 - 2022
Studies abroad

Tilburg University, Netherlands

2021 - 2024
Research Assistant

Methods Center, University of Tübingen

2023
M.Sc. Quantitative Data Science Methods

University of Tübingen

2019 - 2020
Consultant

PricewaterhouseCoopers Aarata LLC, Japan

2019
B.Sc. Mathematical Science and Electrical-Electronic-Computer Engineering

Akita University, Japan

Publications

Presentations at Conferences

Okuyama, K., & Neduchal L., & Brandt, H. (2025, September). "Time-series forecasting in dynamic structural equation models under model uncertainty: Bayesian model stacking approach." Presentation at the FGME Berlin 2025, Freie Universität Berlin, Germany. 
Okuyama, K., & Schaffland T., & Kilian P., & Brandt, H., & Kelava A. (2025, March). "Forecasting regime-switches in intensive longitudinal data." Presentation at the DAGStat 2025, Humboldt-Universität zu Berlin, Germany. 
Okuyama, K., & Schaffland T., & Kilian P., & Brandt, H., & Kelava A. (2024, September). "Forecasting in regime-switching models that emerge from filtering and machine learning." Presentation at the 53rd DGPs Congress, University of Vienna, Austria. 
Okuyama, K., & Schaffland T., & Kilian P., & Brandt, H., & Kelava A. (2024, March). "Frequentist forecasting in regime-switching models using filtering and machine learning." Presentation at the Meeting of the Working Group Structural Equation Modeling, University of Twente, Netherlands. 
Fabbricatore R. &, Okuyama K., & Orsoni M. (2022, July). "Predicting Item Difficulty in Multiplication Games: Linear and Non-linear Models to Detect Feature Effects." Datathon at International Meeting of Psychometric Society (IMPS), University of Bologna, Italy.