Parametric estimation for the location parameter for symmetric distributions using moving extremes ranked set sampling with application to trees data

Al-Saleh, Mohammad Fraiwan; Al-Hadrami, Said Ali

doi:10.1002/env.610

Cited by 45 publications

(15 citation statements)

References 21 publications

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“…In this section, we perform an extensive Monte-Carlo simulation study to compare the average confidence lengths (ACL) and the coverage probabilities (CP) of the confidence intervals constructed in this study. In our simulation setup, we take the set sizes and the number of cycles as (m x , m y ) = (3, 3), (3,4), (4,4), (4,5) and (5,5) and r x = r y = 5 and 10, respectively. Therefore, in the context of RSS, the sample sizes for X and Y are obtained as n = m x r x and m = m y r y .…”

Section: Simulation Studymentioning

confidence: 99%

Interval Estimation of the System Reliability for Weibull Distribution based on Ranked Set Sampling Data

Akgül¹,

Şenoğlu²,

Acıtaş³

2018

HJMS

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Inference for the system reliability R is one of the most popular problems in the areas of engineering, statistics, biostatistics and etc. Therefore, there exist considerable numbers of studies concerning this problem. Traditionally, simple random sampling (SRS) is used for estimating the system reliability. However, in recent years, ranked set sampling (RSS), cost effective and efficient alternative of SRS, is used to estimate the system reliability. In this study, we consider the interval estimation of R when both the stress and the strength are independent Weibull random variables based on RSS. We first obtain the asymptotic confidence interval (ACI) of R by using the maximum likelihood (ML) methodology. The bootstrap confidence interval (BCI) of R is also constructed as an alternative to ACI. An extensive Monte-Carlo simulation study is conducted to compare the performances of ACI and BCI of R for different settings. Finally, a real data set is analyzed to demonstrate the implementation of the proposed methods.

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Section: Simulation Studymentioning

confidence: 99%

Interval Estimation of the System Reliability for Weibull Distribution based on Ranked Set Sampling Data

Akgül¹,

Şenoğlu²,

Acıtaş³

2018

HJMS

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“…Another modification of RSS, namely moving extreme ranked set sampling (MERSS), was introduced by Al-Odat and Al-Saleh [3]. This method was further investigated by Al-Saleh and Al-Hadhrami [6,7], and Al Saleh [5]. The MERSS procedure can be implemented as follows:…”

Section: Introductionmentioning

confidence: 99%

More efficient logistic analysis using moving extreme ranked set sampling

Samawi

Rochani

Linder

et al. 2016

Journal of Applied Statistics

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Logistic regression is the most popular technique available for modeling dichotomous-dependent variables. It has intensive application in the field of social, medical, behavioral and public health sciences. In this paper we propose a more efficient logistic regression analysis based on moving extreme ranked set sampling (MERSS min ) scheme with ranking based on an easy-to-available auxiliary variable known to be associated with the variable of interest (response variable). The paper demonstrates that this approach will provide more powerful testing procedure as well as more efficient odds ratio and parameter estimation than using simple random sample (SRS). Theoretical derivation and simulation studies will be provided.

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“…Al-Saleh and Al-Hadhrami [8] studied the MLE of location distributions based on MERSS. Abu-Dayyeh and Al-Sawi [9] have obtained modified MLE of the mean of exponential distribution using MERSS.…”

Section: Introductionmentioning

confidence: 99%

“…In order to get a closed form expression of the approximate MLE of θ , some terms of the likelihood equation will be replaced by their expectations. This technique was used by Mehrotra and Nanda [10] for studying MLE based on censored data, Zheng and Al-Saleh [11] for MLE with RSS data, and Al-Saleh and Al-Hadhrami [8] [12] and Abu-Dayyeh and Al-Sawi [9] for MLE using MERSS data. In Section 5 we study a modified MLE for estimating the scale parameter of exponential distribution assuming perfect ranking.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Mean of the Exponential Distribution Using Maximum Ranked Set Sampling with Unequal Samples

Biradar¹,

Santosha²

2014

OJS

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In this paper maximum ranked set sampling procedure with unequal samples (MRSSU) is proposed. Maximum likelihood estimator and modified maximum likelihood estimator are obtained and their properties are studied under exponential distribution. These methods are studied under both perfect and imperfect ranking (with errors in ranking). These estimators are then compared with estimators based on simple random sampling (SRS) and ranked set sampling (RSS) procedures. It is shown that relative efficiencies of the estimators based on MRSSU are better than those of the estimator based on SRS. Simulation results show that efficiency of proposed estimator is better than estimator based on RSS under ranking error.

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Parametric estimation for the location parameter for symmetric distributions using moving extremes ranked set sampling with application to trees data

Cited by 45 publications

References 21 publications

Interval Estimation of the System Reliability for Weibull Distribution based on Ranked Set Sampling Data

Interval Estimation of the System Reliability for Weibull Distribution based on Ranked Set Sampling Data

More efficient logistic analysis using moving extreme ranked set sampling

Estimation of the Mean of the Exponential Distribution Using Maximum Ranked Set Sampling with Unequal Samples

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