A ratio chain-type exponential estimator for finite population mean using double sampling

Area of Science:

• Statistics
• Survey Methodology
• Statistical Inference

Background:

• Accurate estimation of finite population parameters is crucial in statistical surveys.
• Traditional methods may lack efficiency when auxiliary information is available.
• Double sampling schemes enhance precision by utilizing auxiliary variables.

Purpose of the Study:

• To propose a novel ratio chain-type exponential estimator for finite population mean.
• To evaluate the efficiency of the proposed estimator under a double sampling scheme.
• To compare the performance against existing estimators in the literature.

Main Methods:

• Development of a ratio chain-type exponential estimator.
• Derivation of large sample properties up to the first order of approximation.
• Conducting an empirical study to assess estimator performance.
• Utilizing auxiliary variables within a two-phase sampling framework.

Main Results:

• The proposed estimator's large sample properties were derived.
• Conditions for the superiority of the proposed estimator were established.
• Empirical evidence confirmed the enhanced efficiency of the new estimator.
• The suggested strategy outperformed competing estimators in two-phase sampling.

Conclusions:

• The proposed ratio chain-type exponential estimator is a valuable addition to survey methodology.
• The estimator offers improved precision for finite population mean estimation.
• The study highlights the benefits of using auxiliary variables in double sampling schemes.