Statistical Analysis and Data Mining

Scope & Guideline

Driving Progress in Statistical Techniques and Applications

Introduction

Welcome to your portal for understanding Statistical Analysis and Data Mining, featuring guidelines for its aims and scope. Our guidelines cover trending and emerging topics, identifying the forefront of research. Additionally, we track declining topics, offering insights into areas experiencing reduced scholarly attention. Key highlights include highly cited topics and recently published papers, curated within these guidelines to assist you in navigating influential academic dialogues.
LanguageEnglish
ISSN1932-1864
PublisherWILEY
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2008 to 2024
AbbreviationSTAT ANAL DATA MIN / Stat. Anal. Data Min.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address111 RIVER ST, HOBOKEN 07030-5774, NJ

Aims and Scopes

The journal 'Statistical Analysis and Data Mining' focuses on advanced statistical methodologies and their applications across various domains. It emphasizes innovative approaches to data analysis, machine learning, and statistical modeling, catering to both theoretical developments and practical implementations.
  1. Statistical Modeling and Inference:
    The journal publishes research on both classical and modern statistical models, including Bayesian methods, nonparametric approaches, and machine learning techniques. This includes developments in regression models, survival analysis, and multivariate analysis.
  2. Data Mining and Machine Learning:
    A core focus on algorithms and techniques for data mining, including classification, clustering, and feature selection, particularly in high-dimensional and complex datasets.
  3. Applications in Various Fields:
    Research that applies statistical methods to real-world problems, particularly in areas such as healthcare, finance, and environmental science, demonstrating the practical utility of statistical analysis.
  4. Uncertainty Quantification and Robustness:
    A unique contribution of the journal is its emphasis on uncertainty quantification in statistical models and the robustness of methodologies under various conditions.
  5. Innovative Computational Techniques:
    The journal explores new computational techniques for statistical analysis, including the use of deep learning, ensemble methods, and advanced optimization strategies.
The journal is increasingly focusing on contemporary themes that reflect the evolving landscape of statistical analysis and data mining. The following emerging areas have gained traction in recent publications, showing their relevance and potential for future research.
  1. High-Dimensional Data Analysis:
    There is a growing emphasis on methodologies designed for high-dimensional datasets, including feature selection and dimension reduction techniques, which are crucial for handling complex data structures.
  2. Machine Learning and Deep Learning Integration:
    Research that combines traditional statistical methodologies with machine learning and deep learning techniques is on the rise, indicating a trend towards hybrid approaches that leverage the strengths of both fields.
  3. Uncertainty Quantification in Statistical Models:
    The increasing focus on quantifying uncertainty in statistical predictions and model outputs reflects a broader recognition of the importance of robustness and reliability in statistical analysis.
  4. Applications of AI and Neural Networks:
    The application of artificial intelligence, particularly neural networks for various tasks such as classification, regression, and anomaly detection is becoming more prominent, as researchers explore their potential in diverse fields.
  5. Data-Driven Approaches in Healthcare and Environmental Studies:
    Emerging themes include the application of statistical methods to pressing real-world issues, particularly in healthcare analytics and environmental data analysis, showcasing the journal's commitment to impactful research.

Declining or Waning

While the journal has a strong emphasis on emerging methodologies and applications, certain themes have shown a decline in interest or frequency of publication over recent years. This indicates a shift in focus towards more contemporary and relevant areas of research.
  1. Traditional Statistical Techniques:
    There has been a noticeable decline in the publication of papers focusing on traditional statistical techniques that do not incorporate modern computational methods or machine learning frameworks.
  2. Simple Regression Models:
    Research centered on basic regression models without the integration of complex features or high-dimensional data analysis appears to be waning, as the field moves towards more sophisticated modeling approaches.
  3. Non-Bayesian Methods:
    The prevalence of non-Bayesian statistical methods has decreased, reflecting a growing preference for Bayesian approaches that offer better flexibility and interpretation in modeling complex data.
  4. Descriptive Statistics and Basic Data Analysis:
    Papers focusing solely on descriptive statistics or basic data analysis without significant methodological advancements or applications are less frequently published, indicating a shift towards more advanced analytical techniques.

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