A New Ratio Estimator in Stratified Random Sampling

By Cem Kadılar, Hulya Cingi. 2005

116 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d2371d7547b7ea211e3d69793bb5b5732fde444e

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In Statistics and Probability

Citations in the corpus

Ratio estimator for the population mean using ranked set sampling (2009, methodology): “Therefore, in the forthcoming studies, we hope to develop a new ratio estimator using RSS in the stratified random sampling, utilizing the methods in the papers Samawi and Siam (2003) and Kadilar and Cingi (2005) .”
A New Estimator of Population Mean in Stratified Sampling (2006, methodology): “Concluding Remarks The new estimator ̂Y P proves more efficient than the the traditional combined ratio estimator ̂YCR and its modification ̂YKC proposed by Kadilar and Cingi (2005), since the condition N + 1 2 > A/ B + C is likely to hold true comfortably.”
Ratio Estimators using Robust Regression (2007, background): “Note that Kadilar and Cingi [8] adapted these classical estimators for simple random sampling to stratifled random sampling, and then Kadilar and Cingi [ 12 ] proposed a new ratio estimator that was always more e‐cient than these adapted estimators in stratifled random sampling.”
Improvement in Variance Estimation in Simple Random Sampling (2007, methodology): “We use the data of Kadilar and Cingi ( 2003 ) in this section.”
A Family of Estimators of Population Mean Using Auxiliary Information in Stratified Sampling (2008, methodology): “…the proposed estimators T10 T11 (Kaur (1985)-type estimator) and T12 over usual unbiased estimator ȳst or T1 T2 (Searls (1964)-type estimator), T6 (Kadilar and Cingi (2005) estimator), T8 (combined ratio estimator), T9 (Shabbir and Gupta (2006) estimator), and T13 (combined regression estimator).”