Efficient class of estimators for finite population mean using auxiliary attribute in stratified random sampling

Area of Science:

• Statistics
• Survey Methodology

Background:

• Accurate estimation of population means is crucial for informed decision-making.
• Existing estimation methods in sample surveys can be improved for greater efficiency.

Purpose of the Study:

• To develop novel and more effective estimators for population means in sample surveys.
• To enhance estimation accuracy by utilizing auxiliary attributes.

Main Methods:

• Modification of existing estimators (Koyuncu, 2013b; Shahzad et al., 2019).
• Introduction of a new class of estimators.
• Derivation of bias and mean squared error expressions (first-degree approximation).
• Empirical investigation to validate theoretical findings.

Main Results:

• The proposed estimators demonstrate superior performance compared to existing methods.
• The new estimators achieve the lowest mean squared error under optimal conditions.
• Empirical results support the theoretical superiority of the developed estimators.

Conclusions:

• The developed estimators offer a significant advancement in survey methodology.
• The use of auxiliary attributes in conjunction with modified estimators improves population mean estimation.
• The findings provide practical tools for more precise statistical inference in sample surveys.