design sampling strategies

This study addresses the combinatorial optimization problem of identifying a balanced sampling design from a large population under a fixed inclusion probability, such that the weighted estimator of an auxiliary variable closely approximates the known population total—a task of exponential complexity. To tackle this challenge, the authors propose a heuristic approach based on genetic algorithms, which iteratively refines the sampling scheme by integrating minimum support designs with candidate samples exhibiting high balance. This method overcomes the limitations of the traditional cube method in achieving balance and substantially enhances sample balance. Consequently, it offers an efficient and practical approximate optimization pathway for large-scale survey sampling and experimental design.

Designing optimal sampling schemes for finite populations is challenging due to complex mathematical constraints and intractable optimization. Method: This paper proposes a novel geometric sampling paradigm based on two-dimensional representation: first-order inclusion probabilities are modeled as adjustable rectangular bars, enabling intuitive parameterization and diverse design generation. We introduce the first geometric visualization framework for sampling design—bypassing traditional reliance on intricate analytical derivations—and incorporate greedy best-first search to jointly optimize entropy maximization and design optimality, without prescribing algorithmic structure. Contribution/Results: The approach significantly enhances design flexibility and computational efficiency. Experiments demonstrate superior performance over classical designs across key metrics—including entropy, balance, and variance control—establishing it as an interpretable, user-friendly, and efficient tool for finite-population sampling.

This work addresses the frequent neglect of sampling strategy design and generalizability in software engineering research, which often undermines the representativeness of empirical findings. To remedy this, the paper introduces a domain-specific language (DSL) that explicitly models complex sampling workflows over code repositories through composable sampling operators, enabling—for the first time—formal specification and reasoning about the generalizability of sampling strategies. Implemented as a fluent Python API, the DSL is integrated with a statistical metric system to quantitatively assess the external validity of sampled datasets. The authors demonstrate the expressiveness and practical utility of their approach by reconstructing and formalizing the sampling procedures from multiple Mining Software Repositories (MSR) studies, thereby validating the framework’s capacity to capture real-world methodological diversity.

This study addresses the lack of rigorous statistical guarantees in sequential sampling for auditing by formulating it as a sequential hypothesis test under sampling without replacement from a finite population. It defines null and alternative hypotheses based on a tolerable deviation rate and constructs exact stopping and decision rules that provide a priori control over both Type I and Type II error probabilities. The work introduces the first sequential audit sampling framework supporting one-sided, two-stage, and truncated designs. Exact boundaries are derived using finite-population error probabilities and efficiently calibrated via Monte Carlo simulation under the least favorable deviation rate. This approach not only ensures pre-specified error control but also accurately estimates expected sample sizes, making it suitable for attribute sampling and tests of controls.

This paper addresses the critical problem of variance reduction via stratified sampling in online A/B testing. We propose an efficient stratification variable subset selection algorithm that dynamically evaluates the marginal contribution of each variable to estimation variance through layer-wise simulation of the stratification process, enabling precise identification of high-information stratification variables—even under multivariate correlation. Unlike conventional approaches relying on pairwise correlation or heuristic filtering, our method directly optimizes for variance minimization, ensuring both theoretical interpretability and computational efficiency. Experiments on synthetic and real-world business datasets demonstrate that our approach reduces estimation variance by 18%–32% on average compared to classical methods such as covariate adjustment and CUPED. This translates into significantly improved statistical power and experimental sensitivity, facilitating faster and more reliable causal inference in production A/B testing environments.

This work proposes a novel sampling design based on minimal tactical configurations to overcome the limitations of existing spatially balanced sampling methods, which rely on cyclic sequences and enforce contiguous sample blocks, thereby restricting design flexibility. By eliminating topological constraints on sample contiguity, the proposed approach expands the feasible design space while maintaining fixed sample size and equal inclusion probabilities. An initialization strategy that accommodates arbitrary sample sizes and incorporates spatial awareness, combined with simulated annealing optimization, effectively reduces expected bias. Experimental results demonstrate that the method outperforms state-of-the-art techniques in terms of distributional fidelity, variable balance, and spatial dispersion, achieving both theoretical optimality and practical superiority.

该研究针对选区重划中的高维组合问题，通过构建和诊断具有适当覆盖和重叠的候选层来实现分层抽样，使用聚类方法形成计划层面的'单词'。

本文评估了网络安全性测量中的采样策略，指出概率抽样比Top N和混合抽样更能提供稳定无偏的估计结果。

本文提出了一种基于Stein变分梯度下降的抽样框架，将完全顺序设计方法转换为批量顺序设计方法，以解决实验设计中的批量选择问题。

This study addresses the limitations of independent and identically distributed (iid) sampling in achieving adequate space-filling and coverage properties for multivariate distribution simulation. We extend quantile stratified sampling to multivariate normal and other multivariate distributions by introducing an ordered log-density evaluation mechanism, which effectively mitigates sample clustering in high-dimensional spaces. Experimental results demonstrate that the proposed method significantly outperforms traditional iid sampling in terms of spatial uniformity and coverage completeness. Consequently, this approach provides a more representative sampling strategy for efficient simulation of complex multivariate distributions, thereby enhancing the reliability of downstream statistical inference.