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In the sample survey theory, the crux of survey practitioners is to provide "accurate" estimators of the parameter of choice. The conventional theory depends on the regression/difference estimators as they correspond to the best linear unbiased (B L U) estimators. This paper suggests some optimal classes of estimators by modifying the conventional estimators under stratified ranked set sampling (S R S S). The characteristics of the suggested estimators are established to the first-order approximation. The performance of the suggested class of estimators under SRSS has been theoretically and experimentally shown to be superior to traditional estimators, particularly regression (B L U) estimators. [ABSTRACT FROM AUTHOR]

Missing data is an obligatory phenomenon in most of the sample surveys and when it is not handled cautiously at beginning of the study, outcomes may result to substantial biases in the survey estimates. Retaining such evidences into cognizance, this paper proffers few possible improved imputation methods to figure out the problem of missing data at the starting of the study and proffers computation procedure of the population mean using simple random sampling. The characteristics of the proffered imputation methods are ascertained till first order approximation pursued by a computational study carried out using several existent and hypothetical data sets. The findings are turned out rather promising showing dominance over the existing imputation methods. Further, apposite advices have been forwarded to the survey professionals for practical applications. [ABSTRACT FROM AUTHOR]

This research article addresses an efficient separate and combined class of estimators for the population mean estimation based on stratified random sampling (StRS). The first order approximated expressions of bias and mean square error of the proposed separate and combined class of estimators are obtained. A comparative study is conducted to determine the efficiency conditions in which the suggested class of estimators outperforms the contemporary estimators. These efficiency conditions are examined through an extensive simulation study by employing a hypothetically drawn symmetrical and asymmetrical populations. The simulation results have shown that the suggested class of estimators is more effective than the other available estimators. In addition, an application of the proposed methods is also presented by examining a real data set. [ABSTRACT FROM AUTHOR]

Ranked set sampling (RSS) has been proven an efficient alternative to simple random sampling (SRS). The use of auxiliary information also helps to improve the efficiency of the estimation procedures. Therefore, to accomplish higher efficiency and discuss the optimality issues, we proffer some optimal classes of estimators under RSS by employing multi-auxiliary information. It is seen that the ordinary mean estimator, traditional regression, and ratio estimators are the subsets of the proffered estimators. The expressions of the bias and mean square error are reported. An analytical comparison under some optimality conditions points out the ascendancy of the proffered classes of estimators over all reviewed works. The theoretical results have been furnished with computational study by employing some artificial and natural populations. The computational results show that the proffered estimators outperform the conventional estimators reviewed in this study. Furthermore, apposite advices are suggested to the survey persons. [ABSTRACT FROM AUTHOR]

The essence of this paper is to propose some efficient combined and separate classes of estimators for estimating population mean Y ¯ under stratified simple random sampling. The bias and mean square error of the proposed classes of estimators are obtained. The proposed estimators are theoretically justified over the conventional mean estimator, classical ratio and regression estimators, Kadilar and Cingi estimators, Shabbir and Gupta estimators, Singh and Vishwakarma estimators, Koyuncu and Kadilar estimators, Singh and Solanki estimator, Yadav et al. estimator, Solanki and Singh estimator and Shahzad et al. estimator. The theoretical findings are extended with a simulation study accomplished over some artificially generated symmetric and asymmetric populations. [ABSTRACT FROM AUTHOR]

This paper considers some efficient classes of estimators for the estimation of population mean using known population proportion. The usual mean estimator, classical ratio, and regression estimators suggested by Naik and Gupta (1996) and Abd-Elfattah et al. (2010) estimators are identified as the members of the suggested class of estimators. The expressions of bias and mean square errors are derived up to first-order approximation. The proposed estimators were put to test against various other competing estimators till date. It has been found both theoretically and empirically that the suggested classes of estimators dominate the existing estimators. [ABSTRACT FROM AUTHOR]

This study investigates some combined and separate log type estimators for the population mean using stratified random sampling. The mean square error expressions of the proposed estimators are determined to the first order of approximation. Some real data sets are considered to assess the performance of the proposed estimators. The numerical results favor the proposition of the proposed estimators over its competitors. [ABSTRACT FROM AUTHOR]

The present article discusses the issue of population mean estimation in the ranked set sampling framework. A modified class of estimators is proffered and compared in the aspect of its efficacious performance with all salient conventional estimators existing to date. Some well-known existing estimators under R S S are recognized as the members of the proffered estimators for appropriately chosen characterizing scalars. The ascendancy of the proposed class of estimators regarding the conventional estimators has been shown through an extensive computational study utilizing some natural and artificially generated populations. [ABSTRACT FROM AUTHOR]

In this manuscript, we propose the combined and separate difference and ratio type estimators of population mean using stratified ranked set sampling. Additionally, several well-known estimators are identified as the sub-class of the suggested estimators. The characteristics of the suggested estimators have been analyzed and their effective performances are compared with the prominent estimators existing till date. Moreover, to prove the credibility of the theoretical findings, an extensive empirical study is administered over some real and hypothetically yielded symmetric and asymmetric populations. [ABSTRACT FROM AUTHOR]

This study introduces some novel classes of estimators using ranked set sampling to evaluate the population mean utilizing additional information on an auxiliary variable. The suggested classes of estimators include mostly prominent estimators and are found to be more efficient regarding the other existing estimators available till date. The efficiency conditions are derived under which the discussed novel estimators dominate over the existing estimators. Further, to justify the propositions of this article, a computational study is executed utilizing some real and hypothetically produced populations and the results are found to be the rather satisfactory showing improvement over all the discussed works. [ABSTRACT FROM AUTHOR]

This paper proposes some optimal classes of estimators under ranked set sampling. We have acquainted some modifications of the difference and ratio type estimators under ranked set sampling, considered by various authors like Hansen et al., Srivastava, and Walsh in simple random sampling. We have demonstrated that the efficiency of the proposed optimal classes of estimators are always better than the other existing estimators. The theoretical results have been supported by a simulation study carried out over an artificially generated population. [ABSTRACT FROM AUTHOR]

This study introduces some modified classes of estimators of population variance in simple random sampling utilizing information on auxiliary attribute which cover traditional mean, ratio and regression estimators and Kadilar and Cingi (2004) type estimators adapted by Singh and Malik (2014) for particular values of characterizing scalars. The expressions of bias and mean square error of the proposed classes of estimators are tabulated up to the first order of approximation. The proposed classes of estimators are theoretically compared with the contemporary estimators existing till date and the efficiency conditions are obtained. Subsequently, the theoretical results are numerically enhanced by using a real data set. [ABSTRACT FROM AUTHOR]

This study develops a novel logarithmic type estimator for estimating the population mean of study variable using the data on two auxiliary attributes in simple random sampling (SRS). The properties like bias and mean square error are derived up to the approximation of order one. The efficiency conditions are determined under which the proposed estimator dominates the classical ratio and regression estimators, Malik and Singh (2013) estimator, Sharma et al. (2015) estimator and Zaman and Toksoy (2019) estimator. Subsequently, a numerical illustrations provided which additionally shows the dominance of the proposed estimator over the other considered estimators. [ABSTRACT FROM AUTHOR]

In this article, we envisage some efficient classes of estimators in double sampling for estimating the population mean utilizing information on supplementary variable along with their properties. The effective execution of the envisaged estimators are examined in comparison to the usual mean estimator, double sampling classical ratio and difference estimators, Srivastava (1967) estimator, Walsh (1970) estimator, Singh and Vishwakarma (2007) estimator, Singh (2011) estimator and Bhushan and Gupta (2019) estimators. In addition, an empirical study is accomplished to enhance the merits of the proposed estimators. [ABSTRACT FROM AUTHOR]

The present paper utilizes auxiliary information to neutralize the effect of non-response for estimating the population mean. Improved ratio type estimators for population mean have been proposed and their properties are studied. These estimators are suggested for both single phase sampling and two phase sampling in presence of non-response. Empirical studies are conducted to validate the theoretical results and demonstrate the performance of the proposed estimators. The proposed estimators are shown to perform better than those used by Cochran (Sampling Techniques (3rd ed), John Wiley & Sons, 1977), Khare and Srivastava (In Proceedings- National Academy Science, India, Section A, 65, 195-203, 1995), Rao (Randomization Approach in Incomplete Data in Sample Surveys, Academic Press, 1983; Survey Methodology 12, 217-230, 1986), and Singh and Kumar (Australian & New Zealand Journal of Statistics, 50, 395-408, 2008; Statistical Papers, 51, 559-582, 2010) under the derived optimality condition. Suitable recommendations are put forward for survey practitioners. [ABSTRACT FROM AUTHOR]