JIRSS-Journal of the Iranian Statistical Society

Scope & Guideline

Innovating Methodologies, Transforming Disciplines.

Introduction

Welcome to the JIRSS-Journal of the Iranian Statistical Society information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of JIRSS-Journal of the Iranian Statistical Society, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN1726-4057
PublisherIRANIAN STATISTICAL SOC
Support Open AccessNo
CountryIran
TypeJournal
Convergefrom 2011 to 2023
AbbreviationJIRSS-J IRAN STAT SO / JIRSS-J. Iran. Stat. Soc.
Frequency2 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressPO BOX 15815-1614, TEHRAN, ISLAM REPUBLIC 00000, IRAN

Aims and Scopes

The JIRSS - Journal of the Iranian Statistical Society aims to advance the field of statistics through the dissemination of innovative research and methodological advancements. The journal focuses on both theoretical and applied statistics, emphasizing interdisciplinary approaches and real-world applications.
  1. Statistical Theory and Methodology:
    The journal publishes research on foundational statistical theories, including estimation, hypothesis testing, and model selection, contributing to the development of robust statistical methodologies.
  2. Applied Statistics:
    Research that applies statistical techniques to real-world problems across various fields, including environmental science, health, and economics, is a core focus, showcasing the practical utility of statistical methods.
  3. Bayesian Statistics:
    There is a significant emphasis on Bayesian approaches, particularly in estimation, prediction, and decision-making processes, highlighting the journal's commitment to modern statistical paradigms.
  4. Stochastic Processes and Modeling:
    The journal explores stochastic models and their applications, particularly in reliability and life data analysis, which are crucial for understanding complex systems and phenomena.
  5. Multivariate Analysis and Copulas:
    Research on multivariate statistical methods, including copula theory and dependence modeling, is prevalent, reflecting the journal's focus on understanding relationships between multiple variables.
  6. Time Series Analysis:
    The journal publishes studies focused on time series modeling, including applications in epidemiology and environmental data, showcasing its relevance in contemporary statistical analysis.
The JIRSS has seen a rise in certain themes that reflect the evolving landscape of statistical research, with particular emphasis on innovative methodologies and applications that address contemporary challenges.
  1. Machine Learning and Statistical Learning:
    There is a growing trend towards integrating machine learning techniques with statistical theory, particularly in predictive modeling and data analysis, indicating a merging of traditional statistics with modern computational methods.
  2. Bayesian Inference and Robustness:
    Recent publications show an increased focus on Bayesian inference, emphasizing robustness and flexibility in statistical modeling, which is becoming increasingly important in real-world applications.
  3. Modeling and Forecasting of Time Series Data:
    The journal has recently published more articles on advanced time series analysis, particularly in the context of real-time data such as epidemiological trends and economic forecasts, reflecting the need for timely decision-making.
  4. Complex Systems and Network Analysis:
    Emerging themes include the analysis of complex systems and networks, showcasing the journal's responsiveness to interdisciplinary research and the application of statistical methods in understanding complex dependencies.
  5. Statistical Applications in Health and Environmental Science:
    There has been a notable increase in research applying statistical methodologies to health and environmental issues, particularly in light of global challenges like the COVID-19 pandemic.

Declining or Waning

While the JIRSS has maintained a strong focus on several core areas, certain themes have shown a decline in prominence over recent years due to shifts in research interests and methodologies within the field.
  1. Traditional Frequentist Methods:
    There has been a noticeable decrease in publications centered on classical frequentist statistical methods, as newer Bayesian and computational approaches gain traction in statistical research.
  2. Basic Descriptive Statistics:
    Papers focusing solely on basic descriptive statistics have become less common, possibly reflecting a shift towards more complex and inferential statistical techniques that provide deeper insights.
  3. Non-Parametric Methods:
    The frequency of publications dedicated to non-parametric statistical methods has waned, indicating a potential shift towards parametric approaches that are often favored in contemporary applications.

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