RMS-Research in Mathematics & Statistics

Scope & Guideline

Exploring New Frontiers in Mathematics and Statistics

Introduction

Delve into the academic richness of RMS-Research in Mathematics & Statistics with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN-
PublisherTAYLOR & FRANCIS LTD
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationRMS RES MATH STAT / RMS Res. Math. & Stat.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND

Aims and Scopes

The journal 'RMS-Research in Mathematics & Statistics' focuses on the intersection of mathematical theories and statistical applications, emphasizing rigorous analytical methods and innovative statistical techniques. It aims to contribute to both theoretical advancements and practical applications across various domains.
  1. Stochastic Processes and Differential Equations:
    Research related to stochastic models, particularly involving differential equations with applications in fields such as finance, engineering, and physics.
  2. Statistical Methodology and Hypothesis Testing:
    Development and analysis of statistical tests, including their convergence properties and applications to small sample sizes, often relevant in social sciences and medical statistics.
  3. Applied Statistics in Epidemiology:
    Utilization of statistical models to understand and predict disease transmission dynamics, particularly relevant in public health contexts.
  4. Innovative Statistical Distributions:
    Exploration and application of new statistical distributions, contributing to the body of knowledge in statistical theory and its practical applications.
  5. Measurement Error and Data Quality:
    Research addressing the challenges of covariate measurement errors, particularly in survey data analysis, enhancing the reliability of statistical conclusions.
  6. Computational Methods in Mathematics and Statistics:
    Application of computational tools like Wolfram Mathematica for solving complex mathematical problems and performing statistical analyses.
The journal has witnessed a rise in interest in several key areas, reflecting current trends in mathematics and statistics. These emerging themes highlight the journal's adaptability to contemporary research needs and societal challenges.
  1. COVID-19 and Public Health Statistics:
    The modeling of disease transmission, particularly regarding COVID-19, has gained prominence, showcasing the journal's responsiveness to global health crises and the need for statistical analysis in epidemiology.
  2. Advanced Statistical Techniques in Small Sample Contexts:
    There is an increasing focus on hypothesis testing methods tailored for smaller contingency tables, indicating a growing interest in improving statistical power and reliability in limited data scenarios.
  3. Stochastic Modelling with Complex Structures:
    Research in stochastic processes, especially those involving nonlinear dynamics and Poisson jumps, is on the rise, reflecting advancements in understanding complex systems.
  4. Innovative Applications of Statistical Software:
    The use of sophisticated software tools for mathematical problem-solving and statistical analysis is trending, highlighting the importance of computational methods in modern research.

Declining or Waning

While 'RMS-Research in Mathematics & Statistics' continues to explore a wide range of topics, some areas have shown a decline in focus over recent years. These waning themes reflect shifts in research priorities and emerging methodologies.
  1. Classical Statistical Methods:
    There has been a noticeable decrease in publications focused on traditional statistical analysis techniques, possibly due to the rise of more complex and computationally intensive methods.
  2. Generalized Linear Models:
    The prevalence of papers centered on classical generalized linear models appears to be waning, as the field moves towards more flexible modeling approaches that can accommodate a wider range of data structures.
  3. Descriptive Statistics:
    Research that primarily focuses on descriptive statistics without substantial inferential components has become less frequent, indicating a trend towards more rigorous inferential analysis.

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