Japanese Journal of Statistics and Data Science Template
Write in a clean editor, then format for Japanese Journal of Statistics and Data Science in one click — DocuGuru applies the official Springer Nature template with superscript references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the Japanese Journal of Statistics and Data Science format
Japanese Journal of Statistics and Data Science is a peer-reviewed journal published by Springer Nature, covering Statistical Methods and Inference, Statistical Methods and Bayesian Inference, Statistical Distribution Estimation and Applications.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. Japanese Journal of Statistics and Data Science 12, 45–58 (2023).
Formats any DOI in Japanese Journal of Statistics and Data Science style. No sign-up. |
| Publishes research in | Statistical Methods and Inference Statistical Methods and Bayesian Inference Statistical Distribution Estimation and Applications Advanced Statistical Methods and Models Bayesian Methods and Mixture Models |
| ISSN | 2520-8756 |
| Citation impact (2-yr) | 0.69 |
| h-index | 14 |
| i10-index | 36 |
| Total citations | 1,233 |
| Article processing charge | $2,990 |
| Top institutions publishing here | The University of Tokyo |
| Journal website | www.springer.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in Japanese Journal of Statistics and Data Science per year
Citation impact of Japanese Journal of Statistics and Data Science by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in Japanese Journal of Statistics and Data Science
Abstract The purpose of this manuscript is to provide a survey on the important methods addressing outliers while producing official statistics. Outliers are often unavoidable in survey statistics. They may reduce the information of survey datasets and distort estimation on each step of the survey statistics production process. This paper defines outliers to be focused…
Abstract Small area estimation is recognized as an important tool for producing reliable estimates under limited sample information. This paper reviews techniques of small area estimation using mixed models, covering from basic to recently proposed advanced ones. We first introduce basic mixed models for small area estimation, and provide several methods for computing mean squared…