Automated Author ProfileFeroze, Nvaid
Feroze, Nvaid
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.7 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Burr model is especially suitable for the life-testing of the products that age with time. Trimmed samples are widely utilized in several areas of statistical practice, especially when some sample values at either or both extremes might have been adulterated. In this article, the problem of estimating the parameter of Burr distribution type II based on trimmed samples under informative and uninformative has been addressed. The problem discussed using Bayesian approach to estimate the shape parameter of Burr type II distribution. Elicitation of hyperparameter through prior predictive approach has also been discussed. Posterior predictive distributions along with posterior predictive intervals and credible intervals have also been derived under different priors. A comparison has been made using the Monte Carlo simulation. A real life data example has also been discussed.
Authors
- Feroze, Nvaid