Modified maximum likelihood estimators using ranked set sampling

Balci, Sibel; Akkaya, Ayşen D.; Ulgen, B. Emre

doi:10.1016/j.cam.2012.08.030

Cited by 24 publications

(8 citation statements)

References 24 publications

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“…x j(j:k)i is the observed value of X j(j:k)i . For more details, see for example, [22] and [6]. Throughout this paper, the product ∏ 0 i=1 is defined to be 1.…”

Section: Preliminariesmentioning

confidence: 99%

A flexible ranked set sampling schemes: Statistical analysis on scale parameter

Eftekharian¹,

Razmkhah

Ahmadi³

2020

Stat., optim. inf. comput.

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A flexible ranked set sampling scheme including some various existing sampling methods is proposed. This scheme may be used to minimize the error of ranking and the cost of sampling. Based on the data obtained from this scheme, the maximum likelihood estimation as well as the Fisher information are studied for the scale family of distributions. The existence and uniqueness of the maximum likelihood estimator of the scale parameter of the exponential and normal distributions are investigated. Moreover, the optimal scheme is derived via simulation and numerical computations.

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“…x j(j:k)i is the observed value of X j(j:k)i . For more details, see for example, [22] and [6]. Throughout this paper, the product ∏ 0 i=1 is defined to be 1.…”

Section: Preliminariesmentioning

confidence: 99%

A flexible ranked set sampling schemes: Statistical analysis on scale parameter

Eftekharian¹,

Razmkhah

Ahmadi³

2020

Stat., optim. inf. comput.

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“…Balci et al [13] introduced modified RSS by choosing paired units from each sample and they have called this sampling scheme as RSS (D).…”

Section: Ranked Set Sampling By Choosing Diagonals Of Samples (Rss (D))mentioning

confidence: 99%

“…Therefore it is sensible to have more than one quantified observations (order statistics) from each sample to construct an estimator or test of a hypothesis. Recently, Balci et al [13] introduced two modified RSS by choosing two elements from each sample. They have studied modified maximum likelihood estimator (MMLE) and best linear unbiased estimator (BLUE) when the underlying distribution is normal.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Population Mean Using Paired Ranked Set Sampling

Biradar¹,

Santosha²

2015

OJS

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In the situation where the sampling units in a study can be easily ranked than quantified, the ranked set sampling methods are found to be more efficient and cost effective as compared to SRS. In this paper we propose an estimator of the population mean using paired ranked set sampling (RSS) method. The proposed estimator is an unbiased estimator of the population mean when the set size is even. In case of odd set size the estimator is unbiased when the underlying distribution is symmetric. It is shown that the proposed estimator is more efficient than its counterpart SRS method for all distributions considered in this study.

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“…A modified MLE (MMLE) using RSS for estimating general parameters of a location-scale distribution was suggested by Zheng and Al-Saleh [28]. Balci et al [9] derived the MMLEs for the population mean and variance under RSS and showed their superiority over the usual RSS estimators. Chen et al [10] considered MLE of the scale parameter of a scale family of distributions using moving extremes RSS (MERSS) scheme.…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood estimation in location-scale families using varied L ranked set sampling

Al-Omari

2021

RAIRO-Oper. Res.

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Recently, a generalized ranked set sampling (RSS) scheme has been introduced which encompasses several existing RSS schemes, namely varied L RSS (VLRSS), and it provides more precise estimators of the population mean than the estimators with the traditional simple random sampling (SRS) and RSS schemes. In this paper, we extend the work and consider the maximum likelihood estimators (MLEs) of the location and scale parameters when sampling from a location-scale family of distributions. In order to give more insight into the performance of VLRSS with respect to SRS and RSS schemes, the asymptotic relative precisions of the MLEs using VLRSS relative to that using SRS and RSS are compared for some usual location-scale distributions. It turns out that the MLEs with VLRSS are more precise than those with the existing sampling schemes.

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Modified maximum likelihood estimators using ranked set sampling

Cited by 24 publications

References 24 publications

A flexible ranked set sampling schemes: Statistical analysis on scale parameter

A flexible ranked set sampling schemes: Statistical analysis on scale parameter

Estimation of the Population Mean Using Paired Ranked Set Sampling

Maximum likelihood estimation in location-scale families using varied L ranked set sampling

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