Annals of Applied Statistics

Scope & Guideline

Charting the Course for Future Statistical Discoveries

Introduction

Delve into the academic richness of Annals of Applied 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
ISSN1932-6157
PublisherINST MATHEMATICAL STATISTICS-IMS
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2008 to 2024
AbbreviationANN APPL STAT / Ann. Appl. Stat.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address3163 SOMERSET DR, CLEVELAND, OH 44122

Aims and Scopes

The Annals of Applied Statistics focuses on the development and application of statistical methods to analyze complex data across various fields. The journal emphasizes innovative methodologies, practical applications, and interdisciplinary approaches.
  1. Statistical Methodology Development:
    The journal publishes papers that introduce new statistical techniques, particularly those addressing complex data structures such as high-dimensional, longitudinal, and survival data.
  2. Applications in Health and Biomedical Research:
    A significant portion of the journal's output is dedicated to statistical applications in health sciences, including epidemiology, clinical trials, and public health, showcasing the importance of statistics in addressing health-related questions.
  3. Bayesian Methods and Machine Learning:
    The journal features contributions that apply Bayesian methods and machine learning techniques to improve data analysis, model fitting, and inference in various research areas.
  4. Environmental and Ecological Statistics:
    Research focusing on statistical applications in environmental science, ecology, and climate studies is prevalent, reflecting the journal's commitment to addressing pressing global issues through statistical lenses.
  5. Social Science Applications:
    The journal also includes studies that apply statistical methods to social sciences, demonstrating the versatility of statistical techniques in understanding societal phenomena.
Recent trends in the journal reveal a shift towards innovative methodologies and applications that respond to contemporary challenges in data analysis.
  1. Integration of Machine Learning with Traditional Statistics:
    There is a growing trend towards integrating machine learning techniques with traditional statistical methods, enhancing model performance and accuracy in various applications.
  2. Focus on Causal Inference:
    The emergence of papers emphasizing causal inference methodologies highlights a significant trend in the journal, reflecting the increasing importance of understanding causal relationships in observational data.
  3. High-Dimensional Data Analysis:
    The journal is witnessing an uptick in research addressing high-dimensional data challenges, particularly in genomics and health data, as researchers seek to extract meaningful insights from complex datasets.
  4. Applications in Public Health and Epidemiology Post-COVID-19:
    The COVID-19 pandemic has spurred a surge in research focusing on public health statistics, with methodologies aimed at understanding and combating infectious diseases gaining prominence.
  5. Spatial and Spatiotemporal Modeling:
    There is a notable increase in the application of spatial and spatiotemporal statistical models, particularly in environmental studies and epidemiological research, reflecting the growing interest in these areas.

Declining or Waning

While the Annals of Applied Statistics continues to thrive in many areas, there are certain themes that appear to be declining in frequency or prominence in recent publications.
  1. Traditional Frequentist Approaches:
    There seems to be a noticeable decrease in the number of papers employing traditional frequentist statistical methods, as newer methodologies, particularly Bayesian and machine learning approaches, gain traction.
  2. Basic Descriptive Statistics:
    Papers focusing solely on descriptive statistics without advanced analytical methods are becoming less common, reflecting an increased expectation for more complex and impactful analyses.
  3. Simple Linear Regression Models:
    The application of straightforward linear regression models is waning, with researchers favoring more intricate models that account for complexities in data.

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