About Journal & Scope
About Journal
The Journal of Statistics for Modern Sciences (JSMS) is established to promote the development and dissemination of statistical knowledge and its interdisciplinary applications. With the rapid growth of data-driven research and digital technologies, statistical methods have become essential tools in almost every scientific domain.
The journal aims to provide a scholarly platform for publishing high-quality research that integrates statistical theory with modern computational techniques and real-world applications.
JSMS encourages research contributions from:
- Applied Statistics
- Statistical Modeling
- Data Science & Machine Learning
- Artificial Intelligence
- Biostatistics & Medical Stats
- Environmental & Ag. Statistics
- Econometrics & Finance
- Industrial Stats & QC
- Statistical Computing
- Big Data Analytics
- Mathematical Statistics
Publication Details
| Journal Title | Journal of Statistics for Modern Sciences |
|---|---|
| Abbreviation | JSMS |
| Subject Area | Applied Sciences |
| Frequency | Three Issues per Year |
| Format | Online |
| Language | English |
| Peer Review | Double Blind Peer Review |
| ISSN No: | 3139-7468 |
| Access | Open Access |
Aim and Scope
Aim: The primary aim of JSMS is to promote innovative research in statistics and its applications in modern sciences, technology, engineering, medicine, and social sciences. The journal focuses on advancing statistical methodologies and their practical implementation in solving complex real-world problems.
Scope areas include, but are not limited to:
Core Statistical Areas
- Mathematical Statistics
- Applied Statistics
- Probability Theory
- Bayesian Statistics
- Multivariate Analysis
- Time Series Analysis
Computational Techniques
- Machine Learning Algorithms
- Artificial Neural Networks
- Deep Learning Applications
- Data Mining Techniques
- Big Data Analytics
- Computational Statistics
Application Areas
- Biostatistics and Public Health
- Medical and Clinical Data
- Environmental Statistics
- Agricultural Statistics
- Econometrics & Financial Analytics
- Social Science Data Analysis