Modified maximum likelihood estimators using ranked set sampling

Date

2012-01-15

Author

Balci, Sibel
Akkaya, Ayşen
Ulgen, B. Emre

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The closed-form maximum likelihood estimators (MLEs) of population mean and variance under ranked set sampling (RSS) do not exist since the likelihood equations involve nonlinear functions and have usually no explicit solutions. We derive modified maximum likelihood (MML) estimators for the population mean and variance under RSS and show that they are considerably more efficient than RSS estimators. Furthermore, we suggest two new estimators for the unknown parameters using two modified ranked set sampling methods and show that these methods make the variances of both MML and RSS estimators smaller.

Subject Keywords

Order statistics, Modified maximum likelihood, Ranked set sampling

URI

https://hdl.handle.net/11511/47238

Journal

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

DOI

https://doi.org/10.1016/j.cam.2012.08.030

Collections

Department of Statistics, Article

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Citation Formats

S. Balci, A. Akkaya, and B. E. Ulgen, “Modified maximum likelihood estimators using ranked set sampling,” JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, pp. 171–179, 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47238.