The New Sub-regression Type Estimator in Ranked Set Sampling.

Area of Science:

• Statistics
• Statistical Inference
• Sampling Techniques

Background:

• Ranked set sampling (RSS) is an efficient data collection method.
• Existing estimators in RSS have limitations in certain scenarios.
• Sub-ratio estimators offer a potential improvement over traditional methods.

Purpose of the Study:

• To introduce a new sub-regression type estimator for ranked set sampling (RSS).
• To theoretically derive and evaluate the mean square error of the proposed estimator.
• To compare the performance of the new estimator against existing ones in the literature.

Main Methods:

• Development of a novel sub-regression estimator based on sub-ratio estimation principles.
• Theoretical derivation of the mean square error (MSE) for the proposed estimator.
• Empirical validation using diverse simulation studies and real-life datasets.

Main Results:

• The proposed sub-regression estimator demonstrates superior effectiveness compared to existing estimators.
• Theoretical MSE analysis confirms the advantages of the new estimator.
• Simulation and real-data results consistently support the superiority of the proposed method.

Conclusions:

• The novel sub-regression estimator offers a significant advancement in statistical estimation for RSS.
• The effectiveness of sub-estimators in RSS is influenced by the number of repetitions.
• The proposed estimator provides a more accurate and efficient alternative for statistical analysis using RSS.