On Bivariate Ranked Set Sampling for Distribution and Quantile Estimation and Quantile Interval Estimation Using Ratio Estimator

2004 ◽  
Vol 33 (8) ◽  
pp. 1801-1819 ◽  
Author(s):  
Hani M. Samawi ◽  
Mohammad Fraiwan Al-Saleh
Keyword(s):  
Interval Estimation ◽  
Ranked Set Sampling ◽  
Quantile Estimation ◽  
Ratio Estimator

Quantile interval estimation in finite population using a multivariate ratio estimator

Metrika ◽  
1998 ◽  
Vol 47 (1) ◽  
pp. 203-213 ◽  
Author(s):  
M. Rueda García ◽  
A. Arcos Cebrián ◽  
E. Artés Rodríguez
Keyword(s):  
Finite Population ◽  
Interval Estimation ◽  
Ratio Estimator

scholarly journals A Comparison of Some Approximate Confidence Intervals for a Single Proportion for Clustered Binary Outcome Data

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Krishna K. Saha ◽  
Daniel Miller ◽  
Suojin Wang
Keyword(s):  
Biomedical Applications ◽  
Profile Likelihood ◽  
Interval Estimation ◽  
Estimating Equation ◽  
Outcome Data ◽  
Biomedical Data ◽  
Ratio Estimator ◽  
Binary Outcome ◽  
Complex Survey ◽  
Complex Survey Data

AbstractInterval estimation of the proportion parameter in the analysis of binary outcome data arising in cluster studies is often an important problem in many biomedical applications. In this paper, we propose two approaches based on the profile likelihood and Wilson score. We compare them with two existing methods recommended for complex survey data and some other methods that are simple extensions of well-known methods such as the likelihood, the generalized estimating equation of Zeger and Liang and the ratio estimator approach of Rao and Scott. An extensive simulation study is conducted for a variety of parameter combinations for the purposes of evaluating and comparing the performance of these methods in terms of coverage and expected lengths. Applications to biomedical data are used to illustrate the proposed methods.

Interval estimation of $$P(X

2018 ◽  
Vol 33 (3) ◽  
pp. 1325-1348 ◽  
Author(s):  
M. Mahdizadeh ◽  
Ehsan Zamanzade
Keyword(s):  
Interval Estimation ◽  
Ranked Set Sampling

scholarly journals Estimation of stress-strength reliability using record ranked set sampling scheme from the exponential distribution

Filomat ◽  
2015 ◽  
Vol 29 (5) ◽  
pp. 1149-1162 ◽  
Author(s):  
Mahdi Salehi ◽  
Jafar Ahmadi
Keyword(s):  
Maximum Likelihood ◽  
Exponential Distribution ◽  
Interval Estimation ◽  
Minimum Variance ◽  
Ranked Set Sampling ◽  
Strength Parameter ◽  
Likelihood Estimator ◽  
Data Set ◽  
Minimum Variance Unbiased Estimator ◽  
Upper Record

In this paper, point and interval estimation of stress-strength reliability based on upper record ranked set sampling (RRSS) from one-parameter exponential distribution are considered. Maximum likelihood estimator (MLE) as well as the uniformly minimum variance unbiased estimator (UMVUE) of stress-strength parameter are derived and their performance are studied. Also, some confidence intervals for stress-strength parameter based on upper RRSS are constructed and then compared on the basis of a simulation study. Finally, a data set has been analyzed for illustrative purposes.

scholarly journals Comparison of two sampling schemes in estimating the stress-strength reliability under the proportional reversed hazard rate model

2020 ◽  
Vol 9 (1) ◽  
pp. 82-98
Author(s):  
Amineh Sadeghpour ◽  
Ahmad Nezakati ◽  
Mahdi Salehi
Keyword(s):  
Hazard Rate ◽  
Interval Estimation ◽  
Minimum Variance ◽  
Ranked Set Sampling ◽  
Sampling Scheme ◽  
Rate Model ◽  
Data Set ◽  
Reversed Hazard Rate ◽  
Sampling Schemes ◽  
Hazard Rate Model

In this paper, point and interval estimation of stress-strength reliability based on lower record ranked set sampling scheme under the proportional reversed hazard rate model are considered. Maximum likelihood, uniformly minimum variance unbiased, and Bayesian estimators of $\mathcal{R}$ are derived and their performances are compared. Various confidence intervals for the parameter $\mathcal{R}$ are constructed, and compared based on the simulation study. Moreover, the record ranked set sampling scheme is compared with ordinary records in case of interval estimations. Finally, a data set has been analyzed for illustrative purposes.

Improved point and interval estimation of the stress–strength reliability based on ranked set sampling

Statistics ◽  
2018 ◽  
Vol 53 (1) ◽  
pp. 101-125 ◽  
Author(s):  
A. Safariyan ◽  
M. Arashi ◽  
R. Arabi Belaghi
Keyword(s):  
Interval Estimation ◽  
Ranked Set Sampling

scholarly journals Even Order Ranked Set Sampling with Auxiliary Variable

2020 ◽  
Vol 18 (2) ◽  
Author(s):  
Muhammad Tayyab ◽  
Muhammad Noor ul-Amin ◽  
Muhammad Hanif
Keyword(s):  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Sampling Scheme ◽  
Ratio Estimator ◽  
Study Variable ◽  
Median Ranked Set Sampling ◽  
Even Order ◽  
Efficient Alternative ◽  
The One

Even order ranked set sampling (EORSS) is a novel proposed ranked set sampling scheme connected with an auxiliary variable correlated with the study variable. This scheme quantifies only the one sampling unit which is at even position from each ranking set by employing specific criteria. The performance of the ratio estimator under EORSS is compared to its contemporary estimators in simple random sampling (SRS), ranked set sampling (RSS), median ranked set sampling (MRSS) and quartile ranked set sampling (QRSS) exploiting the same number of quantified units. The simulation results proved that EORSS is an efficient alternative sampling scheme for ratio estimation than SRS, RSS, MRSS and QRSS.

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