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In this article, we propose logarithmic type direct and synthetic estima- tors for the estimation of domain mean under simple random sampling. The properties such as bias and mean square error of the proposed direct and synthetic estimators are obtained up to first order approximation. The efficiency conditions are obtained under which the proposed direct and synthetic estimators outperform their conventional counterparts. The performance of the proposed direct and synthetic estimators is examined with the help of comprehensive computational study using real and artificially drawn populations. Some appropriate suggestions are also provided to the surveyors. [ABSTRACT FROM AUTHOR]

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]

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]

It is well known that ranked set sampling (RSS) is more efficient than simple random sampling (SRS). Furthermore, the presence of missing data vitiates the conventional results. Only a minuscule amount of work has been conducted under RSS with missing data. This paper makes a modest attempt to provide some efficient difference- and ratio-type imputation methods in the presence of missing values under RSS. The envisaged imputation methods are demonstrated to provide better results than the existing imputation methods. The theoretical results are enhanced by a computational analysis using real and hypothetically generated symmetric (Normal) and asymmetric (Gamma and Weibull) populations. The computational results show that the proposed imputation method outperforms the existing imputation methods in terms of its higher percent relative efficiency. Additionally, the impact of skewness and kurtosis on the efficiency of the suggested imputation methods has also been calculated. [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]

Summary: This article proposes a new efficient logarithmic class of estimators for estimating the population mean in the case of multi‐auxiliary information using ranked set sampling. The bias and mean square error of the proposed estimators are reported up to the first order of approximation. It is demonstrated analytically and numerically that the proposed estimators are always better than their competitors. We accomplished an empirical study using some real populations and a simulation study using some artificially generated populations to support the analytical comparisons between the proposed estimators and their competitors. [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 a novel predictive estimation approach of the population mean based on logarithmic type estimators as predictor under simple random sampling. The bias and mean square error of the proffered predictive estimators are examined to the approximation of order one. The effciency conditions are obtained and the performance of the proffered predictive estimators is examined regarding the contemporary predictive estimators existing till date. Further, a broad computational study is also administered utilizing few real and artificially rendered symmetric and asymmetric populations to exemplify the theoretical results. [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]

This paper introduces some new ratio- and difference-type estimators of the population variance in the presence of random non-response based on Searls (The utilization of a known coefficient of variation in the estimation procedure. J Am Stat Asso. 1964;59:1225–1226) philosophy. The proposed ratio- and difference-type estimators remain better than the estimators obtained by Ahmeda et al. (Estimation of finite population variance in presence of random non-response using auxiliary variables. Infr Mang Sci. 2005;16(2):73–82) in the presence of random non-response using auxiliary variables. The properties (bias and mean square error) of the proposed estimators presented were derived up to the first-order approximation using the Taylor series approach. Conditions for which the new estimators more efficient than other estimators considered in the study were also established. Numerical examples were conducted, and the results revealed that the proposed class of estimators is more efficient than existing 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]

It is an established fact that the existence of measurement errors (M E) vitiates the conventional properties of the estimators. The impact of correlated measurement errors (C M E) was first assessed by Shalabh and Tsai (2017) using classical ratio and product estimators. This article makes a modest attempt to suggest some classes of robust estimators of population mean in the presence of C M E. The bias and mean square error of the suggested estimators are reported with the first-order approximation and the efficiency conditions are derived. The theoretical results are extended with the help of a simulation study executed over a hypothetically rendered population. Apart from this, the effect of M E on the suggested estimators is also studied both theoretically and empirically. • To examine robustness of the proposed estimators under correlated measurement errors. • To provide a comparative study. • To provide an empirical study using real and artificially generated populations. [ABSTRACT FROM AUTHOR]

This study acquaints an efficient class of estimators for variance estimation in simple random sampling. Some existing prominent estimators are established to be the particular cases of the suggested estimators. The mathematical expressions of bias and mean square error of the suggested estimators are determined to the approximation of order one. The efficacious execution of the suggested estimators is examined against all distinguished estimators available till date. Further, numerical illustrations and Monte Carlo simulations are accomplished to extend the findings of the study. [ABSTRACT FROM AUTHOR]

We propose new classes of estimators of population mean under non-response using bivariate auxiliary information. Some improved regression (or difference) type estimators have been proposed in four different situations of non response along with their properties and the expressions for the bias and mean square errors of the proposed estimators are derived under double (two-stage) sampling scheme. The properties of the suggested class of estimators are studied and it is observed that the proposed estimators performed better when compared to conventional estimators proposed by Singh and Kumar (Journal of Statistical Planning and Inference, 140, 2536-2550, 2010b), Shabbir and Khan (Communications in Statistics - Theory and Methods, 42, 4127-4145, 2013) and Bhushan and Naqvi (Journal of Statistics and Management Systems, 18, 573-602, 2015). A comparative study is also conducted both theoretically as well as empirically in order to support the results. [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]

In this paper we have proposed to use two sampling strategies based on the modified ratio estimator using the coefficient of kurtosis of the auxiliary variable by Singh et al [9] for estimating the population mean (total) of the study variable in a finite population. The main objective of this paper is to provide some ideas for unbiased estimation of population mean of the study variable by using prior information on auxiliary variable. A comparative study is made with usual sampling strategies utilizing the availability of the range prior information regarding the optimizing value of the characterizing scalar. Finally some concluding remarks are given and an empirical study is included as an illustration. [ABSTRACT FROM AUTHOR]