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This paper addresses some classes of combined and separate imputation methods (CSIMs) of the population mean under stratified simple random sampling (SSRS) along with their characteristics. To the best of our knowledge, these imputation methods (IMs) have yet not been studied by any author under SSRS, hence these IMs are called 'novel'. In addition, the existing CSIMs are distinguished as the members of the suggested CSIMs, respectively. The theoretical conditions under which the proposed IMs perform better are obtained by comparing the proposed IMs with the existing IMs. To validate the theoretical findings, the numerical and simulation studies are conducted on real and artificial populations, respectively. [ABSTRACT FROM AUTHOR]

In surveys, successive sampling is generally considered to study the same characteristic from one occasion to another so that the change in characteristics over time can be further studied. This paper presents a class of estimators using a novel approach to estimate population mean on two occasions under successive sampling. We have carried out numerical study and simulation study to validate the significance of the proposed class and its members. We have used an optimum unmatched proportion of each class or estimator to compute their respective mean square error during simulation, while other studies considered some fixed value for unmatched units. For large population, the suggested estimation technique is always better for survey practitioners than others. [ABSTRACT FROM AUTHOR]

In real life, situations may arise when the available data are insufficient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has an impact on sample surveys, but small area estimates are especially prone to it. This paper is a basic effort that suggests design based synthetic imputation methods for the domain mean estimation using simple random sampling in order to address the issue of missing data under SAE. The expression of the mean square error for the proposed imputation methods are obtained up to first order approximation. The efficiency conditions are determined and a thorough simulation study is carried out using artificially generated data sets. An application is included with real data that further supports this study. [ABSTRACT FROM AUTHOR]

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]

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]

In survey sampling, several estimation procedures have been proffered by various prominent authors to compute the impact of measurement errors (ME) but the impact of correlated measurement errors (CME) has been computed only by Shalabh and Tsai [Ratio and product methods of estimation of population mean in the presence of correlated measurement errors. Commun Stat Simul Comput. 2016;46(7):5566–5593]. This study provides a novel approach to compute the impact of CME through some logarithmic-type estimators using simple random sampling (SRS). The properties of the proffered estimators have been studied and compared with the properties of the conventional estimators. A numerical study and a broad spectrum simulation study are accomplished over real and artificially generated populations to support the theoretical results. [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]

This article examines the issue of population mean estimation utilizing past and present data in the form of an exponentially weighted moving average (EWMA) statistic under simple random sampling (SRS). We suggest memory-type logarithmic estimators and derive their properties, such as mean-square error (MSE) and bias up to a first-order approximation. Using the EWMA statistic, the conventional and novel memory-type estimators are compared. Real and artificial populations are used as examples to illustrate the theoretical findings. According to the empirical findings, memory-type logarithmic estimators are superior to the conventional mean estimator, ratio estimator, product estimator, logarithmic-type estimator, and memory-type ratio and product estimators. [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 article suggests some novel logarithmic predictive estimators using ranked set sampling for the computation of population mean. The bias and mean square error of the proposed predictive estimators (PPEs) are determined up to the approximation of order one. The performance of the PPEs is examined regarding the contemporary existing predictive estimators (EPEs) utilizing simple random sampling (SRS) and ranked set sampling (RSS) and the efficiency conditions are obtained. Subsequently, a comprehensive computational examination is accomplished based on natural and artificially rendered symmetric and asymmetric populations to extend the theoretical results. [ABSTRACT FROM AUTHOR]

In survey research, various types of estimators have been suggested that consider only the current sample information to compute the unknown population parameters. Therefore, we utilize the past sample information along with the current sample information in the form of hybrid exponentially weighted moving averages to suggest the memory type logarithmic estimators for time-based surveys. The expression of the mean square error of the suggested estimators is determined to the first order of approximation. A relative comparison of the suggested estimators with the existing estimators is performed and efficiency conditions are obtained. Further, a simulation study is accomplished using a hypothetically rendered population and a real data illustration to improve the theoretical results. The results of the simulation study and the real data application exhibit that the consideration of past and current sample information meliorates the efficiency of the suggested 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]

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]

This manuscript considers some improved combined and separate classes of estimators of population mean using bivariate auxiliary information under stratified simple random sampling. The expressions of bias and mean square error of the proposed classes of estimators are determined to the first order of approximation. It is exhibited that under some particular conditions, the proposed classes of estimators dominate the existing prominent estimators. The theoretical findings are supported by a simulation study performed over a hypothetically generated population. [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]

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]

In this article, we introduce the predictive estimation approach for estimating the population mean using modified difference and ratio type predictive estimators in ranked set sampling. The expressions of bias and mean square error of the proffered predictive estimators are reported to the first order of approximation. A comparative study of the proffered predictive estimators with the conventional predictive estimators under simple random sampling and ranked set sampling is considered. The theoretical findings have been supported by a broad spectrum computational study carried out using various real and simulated data sets. Further, the appropriate suggestions are forwarded to the survey professionals. [ABSTRACT FROM AUTHOR]