RG
About
G. R. V. Triveni is an Assistant Professor in the Department of Mathematics at SR University, Warangal, Telangana, India. She earned her Ph.D. from the Department of Mathematics, School of Advanced Sciences, VIT - AP University, Amaravati, Andhra Pradesh, India. Her research interests lie in sampling theory.
She completed her M.Sc. in Mathematics from Andhra University, Andhra Pradesh. She also has working knowledge of R programming.
Employment
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SR University Assistant Professor2026 - Present
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Vellore Institute of Technology University Research Scholar2022 - 2026
Education
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Vellore Institute of Technology University Research Scholar2022 - 2025
Projects & Funding
Projects & funding information is unavailable.
Publications (14)
- Enhanced Imputation Methods Using Bivariate Auxiliary Information in Stratified Sampling: An application on Road Accident Data Save
- Application of Log-Type Estimators for Addressing Non-Response in Survey Sampling Using Real Datasets Save
- Double sampling for Non-Response: A study on combined log-type estimators with an application of solar radiation spectrum Save
- Enhancing precision in breast cancer diagnosis using Tailored Ratio-Integrated Variance Estimation using Neyman allocation Implementation(TRIVENI) Save
- Statistical Evaluation of Comorbidities and Environmental Factors in COVID-19 Outcomes: Risk Measures and Predictive Analysis Using Odds and Hazard Ratios Save
- Evolving techniques for enhanced estimation: A comprehensive survey of stratified sampling and post-stratification methods Save
- Advancing Survey Sampling Efficiency under Stratified Random Sampling and Post-Stratification: Leveraging Symmetry for Enhanced Estimation Accuracy in the Prediction of Exam Scores Save
- Development of novel separate ratio estimator in stratified random sampling with applications on real data Save
- Efficient population mean estimation via stratified sampling with dual auxiliary information: A real estate perspective Save
- Exploring the dependability of Combined Ratio Estimators in Stratified Ranked Set Sampling: Insights from COVID-19 data Save