Abstract
Entropy measures are fundamental measures for quantifying the uncertainty of random variables. In this study, we examine the maximum likelihood estimators (MLE) of five well-known entropy measures: Shannon, Rényi, Havrda- Charvát, Arimoto, and Tsallis, under both Simple Random Sampling (SRS) and Ranked Set Sampling (RSS). We derived the asymptotic bias and variance for these entropy estimators and conducted extensive simulations to assess the performance of SRS and RSS in estimating these entropy measures. The effectiveness of our estimators was demonstrated using breast cancer data.
| Original language | English |
|---|---|
| Pages (from-to) | 1598-1610 |
| Number of pages | 13 |
| Journal | Statistics, Optimization and Information Computing |
| Volume | 14 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jul 1 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Scopus Subject Areas
- Signal Processing
- Statistics and Probability
- Information Systems
- Computer Vision and Pattern Recognition
- Statistics, Probability and Uncertainty
- Control and Optimization
- Artificial Intelligence
Keywords
- 62E10
- 62G30
- 62N01
- 62N02
- Arimoto
- Burr XII
- Havrda-Charvát
- Ranked Set Sampling
- Shannon
- Tsallis; Rényi Entropy
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