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Enhancing precision in breast cancer diagnosis using Tailored Ratio-Integrated Variance Estimation using Neyman
G R V Triveni1, Faizan Danish1, V R K Reddy2
1Department of Mathematics, School of Advanced Sciences, VIT-AP University, Inavolu, Beside AP Secretariat, Amaravati, AP, 522237, India.
Scientific Reports
|October 13, 2025
Summary
This study introduces TRIVENI, a new method for variance estimation in stratified random sampling. TRIVENI improves precision and accuracy by using auxiliary information and optimal sample allocation.
Area of Science:
- Statistics
- Survey Methodology
Background:
- Precise variance estimation is crucial in stratified random sampling, particularly with supplementary information.
- Traditional methods may not fully leverage auxiliary variables for optimal precision.
Purpose of the Study:
- To present TRIVENI (Tailored Ratio-Integrated Variance Estimation employing Neyman Allocation Implementation), an innovative method for enhanced variance estimation.
- To demonstrate the superiority of TRIVENI over traditional estimators in stratified sampling contexts.
Main Methods:
- TRIVENI combines ratio-based modifications with Neyman allocation for sample distribution.
- It utilizes two auxiliary variables to assess their combined impact on population variance estimations.
- Ratio estimation is employed to reduce bias and enhance accuracy using auxiliary information.
Main Results:
- Theoretical derivations and simulation studies confirm TRIVENI's effectiveness.
- TRIVENI demonstrates improved efficiency and precision compared to traditional variance estimators.
- The method shows significant advancements in various stratification scenarios.
Conclusions:
- TRIVENI offers a significant advancement in stratified variance estimation.
- The methodology is a valuable tool for survey sampling experts and researchers seeking improved accuracy and efficiency.
