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Estimating the rubber production in Thailand, the world’s leading rubber supplier, can help the Thai government to prepare for rubber cultivation in policy planning. A transformation technique can be used to improve the efficiency of estimating the average rubber yield by reducing the biases and mean square error. A group of population mean estimators has been suggested under stratified random sampling utilizing a transformed auxiliary variable. The biases and mean square errors of the proposed estimators are investigated. Simulation studies and an application to rubber production data in Thailand have been applied to assess their performances under stratified random sampling where the yield of rubber varies depending upon the region. The results show that the estimates of rubber yields with the proposed estimators had small biases and mean square errors. The best estimator gave an estimated rubber production of 1,140 kilogram/hectare, which is close to the population mean of the yields of rubber [ABSTRACT FROM AUTHOR]

In the contemporary era of information technology, copious amounts of data are ubiquitous, generated across various sectors on a daily basis. Analyzing every unit of data is impractical due to constraints such as limited resources in terms of time, labor, and cost. In such scenarios, survey sampling becomes a recommended approach for extracting information about population parameters. The primary goal of this study is to devise an estimation method for acquiring information about population parameters. We propose an optimal estimator for an improved estimation of the population mean in stratified random sampling by leveraging the information from two auxiliary attributes. The proposed estimator's bias, mean squared error (MSE), and minimum mean squared error are determined up to the first-order approximation. It is demonstrated that, under the derived conditions, the proposed estimator theoretically outperforms existing estimators. Four population are utilized to evaluate both the performance and applicability of the proposed estimator. The percentage relative efficiency (PRE) of proposed estimator for all the populations is 178.389, 142.881, 181.383, and 152.679 respectively. The suggested estimator superior to existing estimators, as demonstrated by the numerical examples.

In this paper, we have described the development of an effective two-phase stratified random sampling estimation procedure in a scrambled response situation. Two different exponential, regression-type estimators were formed separately for different structures of two-phase stratified sampling schemes. We have studied the properties of the suggested strategy. The performance of the proposed strategy has been demonstrated through numerical evidence based on a data set of a natural population and a population generated through simulation studies. Taking into consideration the encouraging findings, suitable recommendations for survey statisticians are prepared for the application of the proposed strategy in real-life conditions. [ABSTRACT FROM AUTHOR]

In this paper, two classes of estimators for the ratio of two population means utilizing auxiliary information in stratified random sampling in the presence of non-response with a known population mean of auxiliary variable have been proposed. For the suggested classes, the expressions for bias and mean square error have been obtained. The members of the suggested classes have been defined. With the help of a simulation study and a real data set, the comparison of the proposed classes of estimators with the relevant estimators has been explored which supports the proposed classes of estimators in comparison to the relevant estimators in terms of minimum mean square error (MSE) and high precision. [ABSTRACT FROM AUTHOR]

Quantile regression is one of the alternative regression techniques used when the assumptions of classical regression analysis are not met, and it estimates the values of the study variable in various quantiles of the distribution. This study proposes ratio-type estimators of a population mean using the information on quantile regression for stratified random sampling. The proposed ratio-type estimators are investigated with the help of the mean square error equations. Efficiency comparisons between the proposed estimators and classical estimators are presented in certain conditions. Under these obtained conditions, it is seen that the proposed estimators outperform the classical estimators. In addition, the theoretical results are supported by a real data application. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of estimating the population variance of a study character in the joint presence of random non-response and measurement errors and its application for estimating variations in biological data. Additional information on two highly positively correlated auxiliary variables has been incorporated to develop a general class of estimators under stratified two-phase sampling scheme. Its properties, in terms of bias and mean square error, have been examined. Optimum strata weights have been determined by employing suitable calibration techniques. Simulations using artificial data, as well as real data involving the variation in prostate specific antigen in different age groups when information about prostrate cancer volume and prostate weight is available, demonstrate the performance of the proposed class of estimators with respect to a contemporary estimator. Relevant R codes have been provided as Appendix. [ABSTRACT FROM AUTHOR]

In the present paper, we have considered separate ratio estimator using calibration approach for the population mean in the presence of non-response using stratified random sampling. We have used the calibration approach to improve the precision of estimate of the population mean by using auxiliary character. In this study, we have used two constraints to find the calibration estimator. In order to determine the variance or mean square error of the suggested estimator, we use Taylor’s linearization method. In order to assess the efficiency of the proposed calibration estimator, we also conduct a simulation study. On the basis of simulation study, it has been observed that the proposed estimator is more efficient than the Dykes et al. (2015) and Khare (2013) estimator. [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]

Quantile regression is one of the alternative regression techniques used when the assumptions of classical regression analysis are not met, and it estimates the values of the study variable in various quantiles of the distribution. This study proposes ratio-type estimators of a population mean using the information on quantile regression for stratified random sampling. The proposed ratio-type estimators are investigated with the help of the mean square error equations. Efficiency comparisons between the proposed estimators and classical estimators are presented in certain conditions. Under these obtained conditions, it is seen that the proposed estimators outperform the classical estimators. In addition, the theoretical results are supported by a real data application.

Sousa et al. [Improved mean estimation of a sensitive variable using auxiliary information in stratified sampling. J Stat Manage Syst. 2014;17(5–6):503–518] presented a ratio estimator for mean estimation in stratified sampling using an additive scrambled response model for a sensitive variable. In order to improve the estimation of mean, this study is motivated to introduce two difference-cum-exponential ratio estimators for mean estimation of a sensitive variable in stratified sampling. The theoretical discussion is presented to show that the two proposed estimators are more efficient than the available estimators including Sousa et al. [Improved mean estimation of a sensitive variable using auxiliary information in stratified sampling. J Stat Manage Syst. 2014;17(5–6):503–518] ratio and regression estimator in certain situations. A real-life application and simulation studies are presented to express the performance of the proposed estimator and existing estimators. The two studies provide evidence that the two proposed estimators perform more efficiently for mean estimation than the other available estimators. [ABSTRACT FROM AUTHOR]

In this study, we discuss mean estimation of sensitive variables in the presence of measurement errors and non-response using Stratified Random Sampling, and present a comparison of the proposed estimator with some existing estimators. [ABSTRACT FROM AUTHOR]

This study focuses on the estimation of population variance of study variable in stratified random sampling using auxiliary information when the observations are contaminated by measurement errors. Three classes of estimators of variance under measurement error are proposed by using the approach of Srivastava and Jhajj [18] for the study variable. The properties of the estimator viz. bias and mean square error of the proposed classes of estimators are provided. The conditions for which proposed estimators are more efficient compared to usual estimators are discussed. It is shown that the proposed classes of estimators include a large number of estimators of the population variance of stratified random sampling and their bias and mean square error can be easily derived.

Stratification of population is a probability sampling design used to increase the precision of estimation. An efficient exponential ratio estimator allows estimating the population mean in stratified random sampling using an auxiliary variable. Its expected bias, expected mean square error, and minimum mean square error are expressed. The conditions for which the estimator is more efficient are obtained. The proposed estimators under stratified random sampling have a lower mean square error than the ratio and the exponential estimators. [ABSTRACT FROM AUTHOR]

In this paper, single-phase mixture regression cum ratio estimators are presented by utilizing auxiliary variables and auxiliary attributes simultaneously under stratified random sampling. Special cases of these estimators are discussed and further mean square errors are extracted mathematically. Also, to observe the properties of proposed estimators, simulation technique is used which shows that the distribution of the proposed estimators is approximately normal. To differentiate the performance of the proposed estimators, an empirical study has been conducted by incorporating quantitative and qualitative characteristics in the form of auxiliary attributes and variables simultaneously. Comparisons are made with single-phase mixture regression cum ratio estimators under simple random sampling. It has been found that the mixture regression cum ratio estimators employing multiple auxiliary variables and attributes, simultaneously, under stratified random sampling are more efficient than mixture regression cum ratio estimator under simple random sampling. [ABSTRACT FROM AUTHOR]

Auxiliary information is frequently used to improve the accuracy of the estimators when estimating the unknown population parameters. The aim of this article is to make improvement over traditional methods (i.e. usual unbiased y, ratio and product estimator) for estimating population mean using various parameters associated with auxiliary variable under stratified random sampling. The expressions for the bias and mean squared error of the proposed estimator are obtained under first order of approximation. It has been found theoretically as well as numerically that the proposed estimator is better than the existing estimators. [ABSTRACT FROM AUTHOR]

Koyuncu and Kadilar proposed an estimator based on single auxiliary variable with complete response in stratified random sampling. In this paper, we extended Koyuncu and Kadilar’s estimator to a more generalized class of estimators using two auxiliary variables in stratified random sampling for the situation of non-response and further introduced its another improved generalized class of estimators. The mathematical conditions under which proposed class of estimators are efficient as compare to Hansen and Hurwtiz estimator, and ratio estimators modified for stratified sampling have been derived. An empirical study has also been carried out to examine the performance of the suggested estimators.

In this paper, we have proposed an estimator of finite population mean in stratified random sampling. The expressions for the bias and mean square error of the proposed estimator are obtained up to the first order of approximation. It is found that the proposed estimator is more efficient than the traditional mean, ratio, exponential, regression, Shabbir and Gupta (in Commun Stat Theory Method 40:199-212, 2011) and Khan et al. (in Pak J Stat 31:353-362, 2015) estimators. We have utilized four natural and four artificial data sets under stratified random sampling scheme for assessing the performance of all the estimators considered here. [ABSTRACT FROM AUTHOR]

This paper suggests a stratified optional randomized response model based on Mangat and Singh (1994) model that has proportional and optimal allocation and larger gain in efficiency. Numerically it is found that the suggested model is more efficient than Kim and Warde (2004) stratified randomized response model and Mangat and Singh (1994) model. Graphical representations are also given in support of the present study.

For the past decades, the estimation of population mean is one of the challenging aspects in sampling survey techniques and much effort has been employed to improve the precision of estimates. In this research work, we proposed a modified ratio-product estimator of population mean of the variable of interest using median and coefficient of variation of the auxiliary variable in stratified random sampling scheme. The expression of bias and MSE of the proposed estimator have been obtained under large sample approximation, asymptotically optimum estimator (AOE) is identified with its approximate MSE formula. Estimator based on "estimated optimum values" was also investigated. Theoretical and empirical comparison of proposed estimator with some other ratio and product estimator justified the performance of the proposed estimator. There is a minimum of 15 percent reduction in the MSE from each of the existing ratio and product estimators considered. Thus most preferred over the existing estimators for the use in practical application. [ABSTRACT FROM AUTHOR]

In this paper new exponential type estimators are suggested for estimating the population mean in stratified random sampling. The biases and mean square errors of the suggested estimators are obtained. Also an empirical study is carried out to show the properties of the proposed estimators. [ABSTRACT FROM AUTHOR]

The article suggests a class of estimators of population mean in stratified random sampling using auxiliary information with its properties. In addition, various known estimators/classes of estimators are identified as members of the suggested class. It has been shown that the suggested class of estimators under optimum condition performs better than the usual unbiased, usual combined ratio, usual combined regression, Kadilar and Cingi (2005), Singh and Vishwakarma (2006) estimators and the members belonging to the classes of estimators envisaged by Kadilar and Cingi (2003), Singh, Tailor et al. (2008), Singh et al. (2009), Singh and Vishwakarma (2010) and Koyuncu and Kadilar (2010). [ABSTRACT FROM AUTHOR]

The present study considers the use of stratified random sampling with Neyman allocation to Mangat (Jour. Ind. Soc. Agril. Statist. 44, 82-87, ) unrelated question randomized response strategy for completely truthful reporting. It has been shown that, for the prior information given, our new model is more efficient in terms of variance (in the case of completely truthful reporting) than Kim and Elam's (Statist. Papers 48, 215-233, ) model. Numerical illustrations and graphs are also given in support of the present study. [ABSTRACT FROM AUTHOR]

This paper deals with estimation of population median in simple and stratified random samplings by using auxiliary information. Auxiliary information is rarely used in estimating population median, although there have been many studies to estimate population mean using auxiliary information. In this study, we suggest some estimators using auxiliary information such as mode and range of an auxiliary variable and correlation coefficient. We also expand these estimators to stratified random sampling for combined and separate estimators. We obtain mean square error equations for all proposed estimators and find theoretical conditions. These conditions are also supported by using numerical examples. [ABSTRACT FROM PUBLISHER]

This article suggests two generalized classes of estimators of population mean using auxiliary information in stratified random sampling. The properties of the suggested classes are derived and asymptotic optimum estimator in each class is identified with its properties. The large numbers of known estimators are members of the suggested classes such as usual unbiased estimator, usual combined ratio estimator (Hansen et al., Am Stat Assoc 41:173-189, ), usual combined product estimator (Kaur, Biom J 27(1):101-105, ), and estimators /classes of estimators due to Kaur (Biom J 26(7):749-753, , Biom J 27(1):101-105, ), Diana (Statistica 53(1):59-66, ), Kadilar and Cingi (Biom J 45(2):218-225, , Commun Stat Theory Methods 34(3):597-602, ), Shabbir and Gupta (Am J Math Manag Sci 25(3-4):293-311, ), Singh and Vishwakarma (Metodologia de Encuestas, Monografico: Incidencias en el trabjo de Campo 7(1):32-40, , Stat Trans 7(6):1311-1325, ), Singh et al. (Stat Pap 49(1):37-58, , SORT 34(2):157-180, ), Tailor (Data Sci Jour 8:182-189, ), Koyuncu and Kadilar (J Stat Plan Inference 139(8):2552-2558, , Pak J Stat 26(2):427-443, ) and Tailor et al. (Commun Korea Stat Soc 18(1):111-118, ). The theoretical and empirical studies are carried out and findings are encouraging and support the soundness of the present study. [ABSTRACT FROM AUTHOR]

This article addresses the problem of estimating the population mean in stratified random sampling using the information of an auxiliary variable. A class of estimators for population mean is defined with its properties under large sample approximation. In particular, various classes of estimators are identified as particular member of the suggested class. It has been shown that the proposed class of estimators is better than usual unbiased estimator, usual combined ratio estimator, usual product estimator, usual regression estimator and Koyuncu and Kadilar (2009) class of estimators. The results have been illustrated through an empirical study. [ABSTRACT FROM PUBLISHER]

In the present study, we propose a new estimator for population mean using Singh et al. (2007) and Malik and Singh (2013) estimators in the case of stratified random sampling when the information is available in form of attributes under non-response. Expressions for the mean squared error (MSE) of the proposed estimators are derived up to the first degree of approximation. The theoretical conditions have also been verified by a numerical example. It has been shown that the proposed estimators are more efficient than the traditional estimators. [ABSTRACT FROM AUTHOR]

The sampling literature contains many examples of estimators of population parameters. In the case of generalization of these estimators, estimation of optimum values is essential. For the optimum estimator, the mean square error equations are minimized with respect to unknown parameters and nonlinear constraints. In this paper an attempt is made to get these optimum estimators of parameters in stratified random sampling using genetic algorithms (GAs). Numerical examples are given to illustrate the algorithms. The results show that the genetic algorithm is more efficient than classical ratio type estimator in stratified random sampling. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating the population proportion π_{S} possessing the sensitive attribute using stratified random sampling. Two alternatives to Kim and Warde [12] stratified randomized response model have been proposed. The proposed models are based on extending Singh [28] models to the case of stratified random sampling. It has been shown that the proposed models are more efficient than Kim and Warde [12] stratified randomized response technique under some realistic conditions in both the cases of completely truthful reporting and that of not completely truthful reporting by the respondents. A practical problem with the use of optimum (Neyman) allocation has been pointed out. Thus, in practice, the use of either proportional allocation or equal allocation has been suggested while estimating proportion of a sensitive attribute using stratified randomized response sampling. Numerical illustrations and graphs are also given in support of the present study. [ABSTRACT FROM AUTHOR]

An exponential family of estimators, which use the information of two auxiliary variables in the stratified sampling, is proposed to estimate the population mean of the variable under study. The mean-squared error of the suggested family of estimators are derived under large sample approximation. The family of estimators in its optimum case is carried out to show the properties of the proposed estimators. [ABSTRACT FROM AUTHOR]

Diana (1993) introduced a family of estimators in the stratified random sampling to estimate the population mean. Following the articles of Diana (1993) and Kadilar and Cingi (2003), we propose a new family of estimators in the stratified random sampling that includes the estimators suggested by Kadilar and Cingi (2003), Shabbir and Gupta (2005), Singh et al. (2008). Up to the first and second order of approximations, we obtain the mean square error (MSE) and the optimum case is discussed. Also an empirical study is carried out to show the properties of the proposed estimators. [ABSTRACT FROM AUTHOR]

Gupta and Shabbir 2 have suggested an alternative form of ratio-type estimators for estimating the population mean. In this paper, we obtained a corrected version for the mean square error (MSE) of the Gupta-Shabbir estimator, up to first order of approximation, and the optimum case is discussed. We expand this estimator to the stratified random sampling and propose general classes for combined and separate estimators. Also an empirical study is carried out to show the properties of the proposed estimators. [ABSTRACT FROM AUTHOR]

A general family of estimators, which use the information of two auxiliary variables in the stratified random sampling, is proposed to estimate the population mean of the variable under study. Under stratified random sampling without replacement scheme, the expressions of bias and mean square error (MSE) up to the first- and second-order approximations are derived. The family of estimators in its optimum case is discussed. Also, an empirical study is carried out to show the properties of the proposed estimators. [ABSTRACT FROM AUTHOR]

In this paper two new estimators, c.v_{1} & c.v_{2} of the finite population coefficient of variation are suggested under stratified random sampling. The comparison has been made using some sets of live data. [ABSTRACT FROM AUTHOR]