Journal of Statistical Research

Aims & Scope

JSR aims to influence methodological aspects of statistics through publication of original contributions in the development, improvement, or understanding of statistical theory.

Methodological submissions are expected to describe new or improved methods of data analysis with analytical and/or simulation-based validation with a comparison to existing methods, and a discussion on how the proposed method will impact the practice of statistics.

It also aims to encourage exciting and innovative applications of advanced statistical methods through publication of challenging innovative practices that examine new methods and demonstrate their application through motivating examples from randomized experiments or observational studies.

Submissions are expected to investigate interesting research questions that solve important problems in society.

Reviews of and tutorials on contemporary statistical methods will also be considered.

View Aims & Scope

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Metrics & Ranking

Journal Rank

Year	Value
2024	22986

Journal Citation Indicator

Year	Value
2024	13

SJR (SCImago Journal Rank)

Year	Value
2024	0.172

Quartile

Year	Value
2024	Q3

h-index

Year	Value
2024	2

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Abstracting & Indexing

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

• Scopus
• Google Scholar

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Subjects & Keywords

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

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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.

• Randomization-based covariance adjustment of win ratios and win odds for randomized multi-visit studies with ordinal outcomes

  Citation: 2

  Authors: ANN MARIE K, ELAINE K, GARY G

• The Impact of Variable Omission on Variable Importance Measures of Cart, Random Forest, and Boosting Algorithms

  Citation: 2

  Authors: W Holmes

• Modeling informative dropout in longitudinal data: A joint model approach

  Citation: 2

  Authors: SRIMANTI, PRITHWISH, ARINDOM

• Debias random forest regression predictors

  Citation: 2

  Authors: Lihua, Prabhashi Withana, John

• Sparse regression with clustered predictors

  Citation: 1

  Authors: Aiden, Danielle, Mohammad S A, Kumer P

• Interval prediction of order statistics and record values using concomitants of order statistics and record values for Morgenstern family of distributions

  Citation: 1

  Authors: Laji, Manoj

• Properties of inverse probability of adherence weighted estimator of the per-protocol effect for sustained treatment strategies under different data-generating mechanisms and adherence patterns

  Citation: 1

  Authors: Lucy, Mohammad Ehsanul, Md Belal

• Change point detection via Gaussian mixture model

  Citation: 1

  Authors: WANGSHU, UTKARSH J, SANJEENA

• Diagnostics for one-way random effects panel data models

  Citation: 0

  Authors: SIBNARAYAN, SUGATA SEN

• Probabilistic canonical correlation analysis for sparse count data

  Citation: 0

  Authors: Lin, Vernon M

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