Statistical Techniques for Data Collection in the Era of Data Enrichment
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 1162
Special Issue Editor
Interests: sampling theory; design of experiments; subsampling and subdata selection; design-based causal inference; statistical inference accounting for data collection; case study of special designs or survey problems
Special Issue Information
Dear Colleagues,
In our data-driven world, the exponential growth of information across industries and research domains has made efficient data collection and processing indispensable for informed decision making and scientific advancement. This Special Issue seeks to explore how advanced data collection techniques can strengthen analytical outcomes.
We invite contributions that examine novel approaches to gathering high-quality data and their critical role in improving the validity and efficiency of statistical and causal conclusions. The scope encompasses social surveys, experimental and quasi-experimental designs, subsampling techniques, and design-based causal inference methods. Of special interest are studies demonstrating how machine learning and AI can optimize data collection processes while maintaining statistical rigor.
The Special Issue encourages submissions that address theoretical foundations, methodological innovations, and practical applications of modern data collection strategies. We welcome research showcasing how these techniques enhance statistical power, reduce uncertainty, and support robust AI-driven analytics in real-world settings. We also encourage novel case studies to show how to collect data in real-world scientific problems.
This Special Issue will focus specifically on the following areas:
- Sampling theory;
- Design of experiments;
- Subsampling and subdata selection;
- Design-based causal inference;
- Statistical inference accounting for data collection;
- Case studies of special designs or survey problems.
We invite researchers to submit original research papers, high-quality case studies, and comprehensive review articles sharing the latest findings and insights in the above areas. All submitted papers will undergo rigorous peer review to ensure scientific rigor, innovation, and practical utility.
Dr. Jun Yu
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- sampling theory
- design of experiments
- subsampling and subdata selection
- design-based causal inference
- statistical inference accounting for data collection
- case studies of special designs or survey problems
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