arXiv 28 Aug 2024 · Econometrics
arXiv:2408.15454 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we address the challenge of sampling in scenarios where limited resources prevent exhaustive measurement across all subjects. We consider a setting where samples are drawn from multiple groups, each following a distribution with unknown mean and variance parameters. We introduce a novel sampling strategy, motivated simply by Cauchy-Schwarz inequality, which minimizes the variance of the population mean estimator by allocating samples proportionally to both the group size and the standard deviation. This approach improves the efficiency of sampling by focusing resources on groups with greater variability, thereby enhancing the precision of the overall estimate. Additionally, we extend our method to a two-stage sampling procedure in a Bayes approach, named BayesSRW, where a preliminary stage is used to estimate the variance, which then informs the optimal allocation of the remaining sampling budget. Through simulation examples, we demonstrate the effectiveness of our approach in reducing estimation uncertainty and providing more reliable insights in applications ranging from user experience surveys to high-dimensional peptide array studies.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Z. Zheng, A. M. Mergaert, I. M. Ong, M. A. Shelef, and M. A. Newton (2021) Mixtwice: large-scale hypothesis testing for peptide arrays by variance mixing | 0.644 | 2 | 2 | 100% |
| 2 | Z. Zheng and C. Liu (2024) Bootstrap matching: a robust and efficient correction for non-random a/b test, and its applications | 0.644 | 2 | 2 | 100% |
| 3 | M. F. Amjadi, M. H. Parker, R. R. Adyniec, Z. Zheng, A. M. Robbins,… (2024) Novel and unique rheumatoid factors cross-react with viral epitopes in covid-19 | 0.405 | 1 | 1 | 100% |
| 4 | Y. Benjamini and Y. Hochberg (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing | 0.405 | 1 | 1 | 100% |
| 5 | J. Bernardo, M. Bayarri, J. Berger, A. Dawid, D. Heckerman, A. Smith… (2007) Bayesian nonparametric latent feature models | 0.405 | 1 | 1 | 100% |
| 6 | B. Efron, R. Tibshirani, J. D. Storey, and V. Tusher (2001) Empirical bayes analysis of a microarray experiment | 0.405 | 1 | 1 | 100% |
| 7 | B. Efron (2007) Correlation and large-scale simultaneous significance testing | 0.405 | 1 | 1 | 100% |
| 8 | A. Gelman (2007) Data analysis using regression and multilevel/hierarchical models | 0.405 | 1 | 1 | 100% |
| 9 | G. Kalton (2020) Introduction to survey sampling | 0.405 | 1 | 1 | 100% |
| 10 | J. K. Kim and M. Park (2010) Calibration estimation in survey sampling | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 20 scored citations.