Online edition: ISSN 2432-6380
[TOP] | [2020] | [2021] | [2022] | [2023] | [2024] | [2025] | [2026] | [Japanese] / [English]
R2026-32
Two-Group Comparison Test for Ordinal Categorical Data Based on Latent Beta Distribution
Ryota Ogata, Tetsuji Ohyama (Kurume Univ.)
pp. 1 - 5
R2026-33
Bayesian Copula Modeling for Competing Risks Data under Dependent Left Truncation
Kota Izumi, Hirofumi Michimae (Kitasato Univ.)
pp. 6 - 11
R2026-34
(See Japanese page.)
pp. 12 - 17
R2026-35
A note on psychological factors associated with absences from bouts of professional sumo wrestlers
Shuhei Ota, Sato Ryosuke (Kanagawa Univ.), Kimotsuki Yukiko, Ogasawara Issei (UOsaka), Iwasaki Susumu (FLC)
pp. 18 - 23
R2026-36
Safety Assessment of the Basic Specifications of a Safety Confirmation Train Control System Based on Fault Tree Analysis
Yudai Shigeta, Shuntaro Sekiyama, Yuki Ota, Takayasu Kitano, Akihiro Gion, Hiroyuki Fujita (RTRI)
pp. 24 - 29
R2026-37
Theories of Failure Prediction Methodologies Based on State Space Modeling and Filtering
Toru Kaise (Univ. of Hyogo)
pp. 30 - 34
R2026-38
Reliability of Systems Subjected to Generalized Nonfatal Marshall-Olkin Type Shocks
Takeru Morimoto, Natsumi Takahashi, Tetsushi Yuge (NDA)
pp. 35 - 40
R2026-39
Hypothesis Testing for Evaluating the Reproducibility of Binary Classification Tests When a Gold Standard is Unavailable
Tetsuji Ohyama (Kurume Univ.)
pp. 41 - 45
R2026-40
Model Selection in Regression Analysis Using Unsupervised Clustering
Masao Ueki (Nagasaki Univ.)
pp. 46 - 49
R2026-41
A Note on a Deterministic Annealing EM Algorithm for Bivariate Bernstein Copulas
Hiroyuki Okamura, Koki Nakayama, Tadashi Dohi (Hiroshima Univ.)
pp. 50 - 55
R2026-42
Change point estimation for Gaussian and binomial time series data with copula-based Markov chain models
Takeshi Emura (Hiroshima Univ.)
pp. 56 - 61
R2026-43
[Invited Talk]
Statistical Methods and Reliability of Decision-Making in Vaccine Efficacy Evaluation
Kohei Ata (KM Biologics), Shintaro Hirano, Yoji Ito (A2 Healthcare)
pp. 62 - 67
Note: Each article is a technical report without peer review, and its polished version will be published elsewhere.