Scholar4AI Updated 2026-10-30 12:46 UTC+09:00

Japanese Journal of Statistics and Data Science

Share: X LinkedIn Bluesky Threads Facebook Reddit Hacker News Hatena LINE Email
Copy:

2 papers in the last 7 days · category: Statistics · publisher: Springer Science+Business Media · ISSN 2520-8756

← Papers per journal

Key findings#

  • Japanese Journal of Statistics and Data Science published 5 papers in the last 30 days (2026-10-01 to 2026-10-30), ranking #184 of 392 journals.
  • Leading institutions: Japan Science and Technology Agency (1), Center for Mathematical Modeling and Data Science, The University of Osaka (1) and The University of Osaka (1).
  • Countries: Japan (2), United Kingdom (1) and India (1).
  • From Oct 2023 to Sep 2026, Japanese Journal of Statistics and Data Science published 155 papers (4 a month on average, by publication date in OpenAlex).
  • Its most frequent keywords over that period were “maximum likelihood estimation” (12), “shrinkage estimation” (10) and “asymptotic properties” (9).
  • Its most-cited paper of the period is “A new bivariate survival model with a cured fraction: a mixed Poisson frailty–copula approach” (2024-02-10), with 8 citations.
  • Since October 2016, its most-cited paper is “Statistical data integration in survey sampling: a review” (2020-10-15), with 80 citations.

The last 36 months#

Oct 23: 2 papersNov 23: 5 papersDec 23: 5 papersJan 24: 6 papersFeb 24: 5 papersMar 24: 3 papersApr 24: 2 papersMay 24: 4 papersJun 24: 6 papersJul 24: 5 papersAug 24: 6 papersSep 24: 4 papersOct 24: 5 papersNov 24: 1 papersDec 24: 6 papersJan 25: 8 papersFeb 25: 6 papersMar 25: 2 papersApr 25: 2 papersMay 25: 4 papersJun 25: 1 papersJul 25: 1 papersAug 25: 3 papersSep 25: 4 papersOct 25: 1 papersNov 25: 2 papersDec 25: 3 papersJan 26: 5 papersFeb 26: 5 papersMar 26: 4 papersApr 26: 8 papersMay 26: 4 papersJun 26: 10 papersJul 26: 5 papersAug 26: 9 papersSep 26: 3 papers
Oct 23Sep 26
Papers by publication date, Oct 2023 to Sep 2026
Oct 23–Sep 24Oct 24–Sep 25Oct 25–Sep 2636 months
534359155
Most frequent keywords, Oct 2023 to Sep 2026
#KeywordPapers
1maximum likelihood estimation12
2shrinkage estimation10
3asymptotic properties9
4parameter estimation9
5consistency8
6Bayesian estimation7
7feature selection5
8high-dimensional data5
9artificial neural networks4
10asymptotic confidence intervals4

Most-cited since October 2016# All journals →

The journal's most-cited papers published since October 2016
#CitationsPer monthTitleJournalPublishedAuthors
1801.1Statistical data integration in survey sampling: a reviewJapanese Journal of Statistics and Data Science2020-10-15Shu Yang, Jae Kwang Kim
2520.5Data science vs. statistics: two cultures?Japanese Journal of Statistics and Data Science2018-05-14Iain D. Carmichael, James Stephen Marron
3490.7Outliers in official statisticsJapanese Journal of Statistics and Data Science2020-10-24Kazumi Wada
4410.8Uniform consistency and uniform in number of neighbors consistency for nonparametric regression estimates and conditional U-statistics involving functional dataJapanese Journal of Statistics and Data Science2022-05-28Salim Bouzebda, Amel Nezzal
5310.7General tests of conditional independence based on empirical processes indexed by functionsJapanese Journal of Statistics and Data Science2023-02-25Salim Bouzebda
6300.4Small area estimation with mixed models: a reviewJapanese Journal of Statistics and Data Science2020-04-01Shonosuke Sugasawa, Tatsuya Kubokawa
7290.6R package DCchoice for dichotomous choice contingent valuation: a contribution to open scientific software and its impactJapanese Journal of Statistics and Data Science2022-07-26Hideo Aizaki, Tomoaki Nakatani, Kazuo Sato et al.
8250.3A copula-based Markov chain model for serially dependent event times with a dependent terminal eventJapanese Journal of Statistics and Data Science2020-09-22Xin-Wei Huang, Weijing Wang, Takeshi Emura
9240.4Effective estimation algorithm for parameters of multivariate Farlie–Gumbel–Morgenstern copulaJapanese Journal of Statistics and Data Science2021-04-19Shuhei Ota, Mitsuhiro Kimura
10220.3Likelihood analysis and stochastic EM algorithm for left truncated right censored data and associated model selection from the Lehmann family of life distributionsJapanese Journal of Statistics and Data Science2021-03-21Debanjan Mitra, Debasis Kundu, N. Balakrishnan
Papers, newest first.
DateTitleJournalAuthorsAffiliations
2026-10-26Quasi-BIC of SEM for jump-diffusion processes based on high-frequency dataJapanese Journal of Statistics and Data ScienceShogo Kusano, Masayuki UchidaKumamoto University; Japan Science and Technology Agency; Center for Mathematical Modeling and Data Science, The University of Osaka; …
2026-10-26Local increment inference for time-inhomogeneous drift in Gaussian processesJapanese Journal of Statistics and Data ScienceYasutaka ShimizuWaseda University
2026-10-20Sequential Monte Carlo sampling with integrator snippetsJapanese Journal of Statistics and Data ScienceChristophe Andrieu, Mauro Camara Escudero, Chang ZhangUniversity of Bristol
2026-10-11Quantile-based nonparametric estimation of Matusita’s measureJapanese Journal of Statistics and Data ScienceAngel Mathew, Chinu JosephMaharaja's College, Ernakulam
2026-10-09Dynamic hazard models for multipath changepoint problems: theory, inference, and application to cognitive declineJapanese Journal of Statistics and Data ScienceMasoud MajidizadehShahid Beheshti University
Cite this page

Scholar4AI. (2026). Japanese Journal of Statistics and Data Science. Journal Research Rankings. Retrieved October 30, 2026, from https://scholar4ai.com/journals/japanese-journal-of-statistics-and-data-science

@misc{scholar4ai-journals-japanese-journal-of-statistics-and-data-science,
  author = {{Scholar4AI}},
  title = {Japanese Journal of Statistics and Data Science},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/journals/japanese-journal-of-statistics-and-data-science}},
  urldate = {2026-10-30},
  note = {Journal Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).