Proportion estimation in ranked set sampling in the presence of tie information

Zamanzade, Ehsan; Wang, Xinlei

doi:10.1007/s00180-018-0807-x

Cited by 24 publications

(17 citation statements)

References 16 publications

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“…We have seen that isotonic estimators provide good results from studies on this subject, such as Frey (2012) and Zamanzade and Wang (2018). In addition, we know that ratio estimators give better results than the sample mean estimator.…”

Section: Proposed Estimatorsmentioning

confidence: 94%

Ratio estimators for ranked set sampling in the presence of tie information

Koçyiğit

Kadılar

2020

Communications in Statistics - Simulation and Computation

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In this article, we study the situation of observations whose ranks cannot be determined for the auxiliary variable in the Ranked Set Sampling (RSS) method. Therefore, we examine the case of tie information for ratio estimators of the population mean. We propose a new exponential ratio estimator using the modified isotonic estimator for this situation. Simulation results show that the proposed estimator is more efficient than the other estimators in literature. In addition, when we examine the recent COVID-19 pandemic situation, we see that the data is suitable for this structure. We can also see from the real data that the proposed estimator gives better results.

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Section: Proposed Estimatorsmentioning

confidence: 94%

Ratio estimators for ranked set sampling in the presence of tie information

Koçyiğit

Kadılar

2020

Communications in Statistics - Simulation and Computation

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“…The population of interest is considered to have three ordinal categories. To generate concomitant variables with specified correlation level with the variable of interest, we extend the technique of Zamanzade and Wang 22 proposed for binary populations. To do so, let Y∼Multi(1,p0,p1,p2) with three ordinal categories q∈true{0,1,2true}, where p=(p0,p1,p2) denote their corresponding population proportions and ρ representing the correlation between Y and concomitant variable X .…”

Section: Numerical Analysismentioning

confidence: 99%

“…19 A significant amount of literature has developed different non-parametric and maximum likelihood (ML) estimators based on RSS data for binary populations. 10,[20][21][22] Despite this rich literature in binary population, there are very few research works regarding the categorical population. Although a number of RSS-based estimation procedures was developed for the problem of the ordinal categorical population, 23,24 the proposed methods are not capable of using ranking information of multiple concomitant variables.…”

Section: Introductionmentioning

confidence: 99%

Estimation of ordinal population with multi-observer ranked set samples using ties information

Alvandi

Hatefi

2021

Stat Methods Med Res

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In many surveys, we often deal with situations where measuring the study variable is expensive; however, there are easy-to-measure characteristics which can be used as ranking information to obtain more representative samples from the population. Ranked set sampling is successfully employed in these cases as an alternative to commonly used simple random sampling. When the data is ordinal categorical, it is common to apply the ordinal logistic regression approach to ranked set sampling data for the estimation of parameters. This technique first depends on the information of training data. Besides, one is not capable of using the ranking information in the estimation process. In this paper, we propose a ranked set sampling scheme in which ranking information from multiple sources can be combined and incorporated efficiently into both data collection and estimation. The ranked set sampling data is used for non-parametric and maximum likelihood estimation of ordinal categorical population. Through extensive simulation studies, the performance of estimators is evaluated. The methods are finally applied to analyze bone disorder data and obesity data.

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“…An alternative method for situations in which abundance of auxiliary information is available is ranked set sampling (RSS) method due to McIntyre [3], which is shown is far more cost-efficient than simple random sampling method. See Adel Rastkhiz et al [4], Zamanzade and Vock [5], Zamanzade and Wang [6], Zamanzade and Mahdizadeh [7], Zamanzade and Wang [8] and Mahdizadeh and Zamanzade [9] for more information about this.…”

Section: Introductionmentioning

confidence: 99%

Quantile regression-ratio-type estimators for mean estimation under complete and partial auxiliary information

et al. 2020

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Traditional ordinary least square (OLS) regression is commonly utilized to develop regression-ratiotype estimators with traditional measures of location. Abid et al. [1] extended this idea and developed regression-ratio-type estimators with traditional and non-traditional measures of location. In this article, the quantile regression with traditional and non-traditional measures of location is utilized and a class of ratio type mean estimators are proposed. The theoretical mean square error (MSE) expressions are also derived. The work is also extended for two phase sampling (partial information). The pertinence of the proposed and existing group of estimators is shown by considering real data collections originating from different sources. The discoveries are empowering and prevalent execution of the proposed group of estimators is witnessed and documented throughout the article.

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Proportion estimation in ranked set sampling in the presence of tie information

Cited by 24 publications

References 16 publications

Ratio estimators for ranked set sampling in the presence of tie information

Ratio estimators for ranked set sampling in the presence of tie information

Estimation of ordinal population with multi-observer ranked set samples using ties information

Quantile regression-ratio-type estimators for mean estimation under complete and partial auxiliary information

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