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

Department of Statistics, School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China
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

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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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Published Papers (2 papers)

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Research

18 pages, 872 KB  
Article
Optimal Designs for Multi-Group Linear Models with Measurement Errors
by Min-Jue Zhang, Min-Qian Liu and Xue-Ping Chen
Mathematics 2026, 14(6), 974; https://doi.org/10.3390/math14060974 - 13 Mar 2026
Viewed by 264
Abstract
Multi-group linear models with measurement errors are frequently employed in situations where covariates cannot be precisely measured, thereby compromising the validity of between-group comparisons. However, the study of experimental design theory for these models is currently at an underdeveloped stage. This paper is [...] Read more.
Multi-group linear models with measurement errors are frequently employed in situations where covariates cannot be precisely measured, thereby compromising the validity of between-group comparisons. However, the study of experimental design theory for these models is currently at an underdeveloped stage. This paper is concerned with the problem of constructing locally c-, DA- and D-optimal designs of multi-group linear models with measurement errors for estimating parameters or contrasts in the model parameters. Equivalence theorems are established to confirm the optimality of the designs for such models under each criterion, and the generalization of Elfving’s theorem is proved to describe the geometrical characterization of locally c-optimal designs for such models. Furthermore, the locally D-optimal designs for a class of multi-group linear models with measurement errors can be explicitly determined. It is shown that the locally D-optimal design for such models is given by the product of the locally D-optimal designs for linear measurement error models corresponding to those groups. To illustrate these concepts, several examples are provided. Full article
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25 pages, 480 KB  
Article
Prospective Inference of Central Tendency Through Data-Adaptive Mechanisms
by Huda M. Alshanbari and Malik Muhammad Anas
Mathematics 2025, 13(22), 3622; https://doi.org/10.3390/math13223622 - 12 Nov 2025
Cited by 1 | Viewed by 539
Abstract
In the modern age of data enrichment, it has become necessary to incorporate adaptive inference processes into survey-based estimation systems in order to achieve efficient and consistent population summaries. In this work, a new type of data-adaptive approach to the prospective estimation of [...] Read more.
In the modern age of data enrichment, it has become necessary to incorporate adaptive inference processes into survey-based estimation systems in order to achieve efficient and consistent population summaries. In this work, a new type of data-adaptive approach to the prospective estimation of central tendency under stratified random sampling (StRS) frameworks is presented. The suggested structure takes advantage of the auxiliary information based on locally tuned, non-parametric smoothing plans that dynamically adapt to a heterogeneity of sampled and unsampled domains. The estimator wisely reacts to an intricate pattern of the data, ensured by the application of variable bandwidth functions, stratified weighting plans, which ensure resilience to model misspecification and outlier effects. Substantial Monte Carlo simulations and two empirical studies, i.e., solar radiation data and fish market data, are performed to confirm its performance in a variety of bandwidth and sample size settings. The findings have consistently shown that the suggested adaptive inference mechanism is significantly more precise and stable than traditional estimators, not only when auxiliary expectations are known, but also when they have to be estimated. This study brings into play a flexible, design-conscious framework that connects model-driven estimation with design-driven survey inference, which is of importance in contemporary information-gathering settings of informational diversity and enrichment. Full article
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