Journal of Statistical Planning and Inference

Aims & Scope

The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject.

While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists.

We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics.

We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics.

Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Decision Sciences and Mathematics, designed to support cutting-edge academic discovery.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Minimax and maximin distance designs

  Citation: 1105

  Authors: M.E., L.M., D.

• Improving predictive inference under covariate shift by weighting the log-likelihood function

  Citation: 971

  Authors: Hidetoshi

• Exploratory designs for computational experiments

  Citation: 917

  Authors: Max D., Toby J.

• Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics

  Citation: 578

  Authors: Daniel, Yoav

• An efficient algorithm for constructing optimal design of computer experiments

  Citation: 505

  Authors: Ruichen, Wei, Agus

• Optimal Latin-hypercube designs for computer experiments

  Citation: 499

  Authors: Jeong-Soo

• Energy statistics: A class of statistics based on distances

  Citation: 459

  Authors: Gábor J., Maria L.

• Formal mode of statistical inference for causal effects

  Citation: 336

  Authors: Donald B.

• Algorithmic construction of optimal symmetric Latin hypercube designs

  Citation: 334

  Authors: Kenny Q, William, Agus

• Bayesian emulation of complex multi-output and dynamic computer models

  Citation: 311

  Authors: Stefano, Anthony