The Journal of Statistical Planning and Inference is dedicated to advancing the theory and methods of statistical planning and inference. Its primary focus lies in the development of original research related to the design of experiments, sampling theory, and statistical inference. Covering a broad spectrum of topics, the journal addresses areas such as optimal designs, model selection, estimation theory, hypothesis testing, and Bayesian inference, making it relevant to both theoretical and applied statisticians. Relevant keywords include probability, mathematics, and mathematical modeling.
This journal serves as a valuable resource for researchers and practitioners seeking to stay at the forefront of statistical methodology. Indexing in databases such as Scopus and Web of Science ensures wide visibility. It welcomes contributions that offer novel insights and methodologies applicable to diverse scientific and engineering disciplines.
Researchers are encouraged to submit high-quality manuscripts that contribute to the theoretical foundations and practical applications of statistical planning and inference. By publishing cutting-edge research, the journal aims to foster innovation and collaboration within the statistical community.