Bağ Bilgisi Olduğunda Sıralı Küme Örneklemesinde Yeni Tahmin Ediciler
Özet
Ranked set sampling is a frequently used sampling method developed as an alternative
to the simple random sampling. In this sampling method, rankers are expected to rank
the units within the set correctly, even with low confidence. Also, if there are two or
more very similar or identical units in a set, this makes ranking more difficult and it
causes the units in the set to be ranked incorrectly. In this thesis, the mean estimators
are examined under a method (RSS-t) in which the ranking error, occurred while
ranking with the aid of the auxiliary variable, is reduced by using the tie information
under the Ranked Set Sampling. The study aims to examine the ties in the ranking and
to use these ties in the population mean estimators for more reliable estimates by
minimizing the ranking error. After examining the method and the estimators in the
literature, it is seen that the ratio estimators have not been examined under this method
and therefore new modified raito estimators are proposed in this thesis study. The
effectiveness of the estimators is first calculated for the samples drawn from large and
iv
small populations derived from simulation. Simulation studies have shown that the
proposed estimators are more effective than other estimators in the literature. In
addition, when the variables of the number of diagnosed patients and the number of
patients who died are examined in a real data set of the recently emerging COVID-19
epidemic, it is seen that the data set is suitable for the tie information structure. Similar
to the simulation results, we can also see from the real data set that the proposed
estimators give better results.
