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In survey research, it is commonly assumed that all the observations are measured accurately. However, in practice, this assumption is not achieved due to many reasons, so it causes measurement errors to be inevitably present in the sample estimation. This article addresses the problem of estimation of finite population mean under stratified random sampling in the presence of measurement errors. A calibration estimator is proposed for the sensitive variable by utilizing suitable calibration techniques, which can be applied to obtain the optimal strata weight. The calibration estimator not only incorporates the auxiliary information but also the ranks of the auxiliary variable. Expressions for bias and mean square error are derived up to first order of approximation. Simulation studies and real-life datasets are used to assess the performances of the proposed estimator by comparing them with the contemporary estimators in the presence and absence of measurement errors. The theoretical and empirical studies demonstrate that the proposed calibration estimator consistently outperforms classical mean, usual ratio, conventional difference, Khail randomized response estimators. [ABSTRACT FROM AUTHOR]

In this article, we have derived a new calibration estimator of the population mean in the stratified random sampling under the presence of non response. The proposed calibration estimator has been obtained by making some improvements in the Hansen and Hurwitz (1946) estimator. For this purpose, we have gainfully utilized the information on a single auxiliary variable to obtain a set of new calibrated weights that increase the precision of the estimates. The proposed calibration estimator has been derived using the chi-square type distance function subject to some calibration constraints based on the auxiliary information. The Taylor linearization technique has been used to derive the expression for the variance of the proposed calibration estimator. We have also performed an empirical study based on the hypothetically generated data and real data to study the performance of the proposed calibration estimator. The proposed calibration estimator has been found more efficient than the usual Hansen and Hurwitz (1946) estimator and the recent calibration estimator proposed by Dykes et al. (2015). [ABSTRACT FROM AUTHOR]

An improved ratio-type calibrated estimator was developed using the logarithmic mean in the calibration constraint for the stratified random sampling scheme. The proposed estimator was extended in the case of stratified double sampling and compared with the estimators given by Tracy et al. (2003) together its ratio-type estimators, as well as Nidhi et al. (2017) and Khare et al. (2022). A simulation study was also carried out on both a real and artificial dataset in order to evaluate the performance of the proposed estimator compared to the existing estimators. [ABSTRACT FROM AUTHOR]

In this paper, Randomized Response technique (RRT) is used to propose some separate and combined variance estimators for a sensitive variable using stratified random sampling. The performances of the proposed estimators are examined using a unifed measure of respondent privacy and estimator efficiency.

This paper suggests a calibration ratio type estimator of the population mean in case of the stratified random sampling using a logarithmic mean as a calibration constraint. The result so obtained has been extended in case of stratified double sampling. The simulation study has also supported the performance of the suggested estimators over the existing estimators given by Singh et al. (1998) and Clement (2015) on the basis of two artificial data sets. [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]

This paper considers exponential ratio-type calibration estimators for finite population mean using median of the auxiliary variable in the calibration constraint under stratified random sampling. For judging the performance of the proposed estimators, a simulation study has been carried out on real as well as artificial datasets using R-software and their percentage root mean squared errors have been computed. The suggested estimators have been compared with the estimators given by Singh (2003) and Tracy et al. (2003). [ABSTRACT FROM AUTHOR]

This article suggests a logarithmic calibration estimator of the population mean in stratified random sampling and the result so obtained has been extended in case of stratified double sampling for estimating the population parameter. In order to check the performance of the suggested estimator with the estimator given by Tracy et al. (2003), a simulation study has been carried out on two real datasets. [ABSTRACT FROM AUTHOR]

During a survey study, an investigator may be incapable of assembling the complete response (i.e., there is non-response) and/or the assembled response is not 100 % true (i.e., measurement errors exist). In this situation, estimation of the population mean under stratified random sampling is not an easy task. Mostly these non-sampling errors, i.e. non-response and measurement error, significantly affect the estimators than sampling errors. To deal with this task, a progressive generalized estimator has been proposed, that can generate a number of estimators based on the availability of conventional and/or non-conventional auxiliary information. Ratio-type, ratio-type exponential, ratio-ratio-type exponential, ratio-product-type exponential, product-type, product-type exponential, product-product-type exponential and productratio-type exponential estimators are generated through the proposed generalized estimator. Mathematical properties such as bias, mean squared error and minimum mean squared error of the proposed estimator are derived up to first degree of approximation. The empirical performance of all the estimators in terms of percent relative efficiency is evaluated with the help of a simulation study. It turned out that the proposed estimators outperform when compared with Hansen and Hurwitz (1946) estimator and other competing estimators in this study i.e. Singh and Kumar's (2008), Kumar et al. (2015), Azeem and Hanif (2017) and Zahid and Shabbir (2018). It is suggested that the proposed estimators will be applied in case of non-response and measurement errors under stratified random sampling. [ABSTRACT FROM AUTHOR]

In this article, we investigated estimators with the exponential function for the estimation of the population mean in the simple and stratified random samplings. Family of estimators based on the exponential function is proposed for both sampling methods. The proposed estimators are compared with estimators in literature. Moreover, we provide an application on different data sets to demonstrate the efficiency of the proposed estimators. As a result, the proposed estimators are more efficient than other estimators in literature under the obtained conditions in theory. [ABSTRACT FROM AUTHOR]

In the present article, an effort has been made to develop calibration estimators of the population mean under two-stage stratified random sampling design when auxiliary information is available at primary stage unit (psu) level. The properties of the developed estimators are derived in-terms of design based approximate variance and approximate consistent design based estimator of the variance. Some simulation studies have been conducted to investigate the relative performance of calibration estimator over the usual estimator of the population mean without using auxiliary information in two-stage stratified random sampling. Proposed calibration estimators have outperformed the usual estimator without using auxiliary information. [ABSTRACT FROM AUTHOR]

This article proposes new regression-type estimators by considering Tukey-M, Hampel M, Huber MM, LTS, LMS and LAD robust methods and MCD and MVE robust covariance matrices in stratified sampling. Theoretically, we obtain the mean square error (MSE) for these estimators. We compare the efficiencies based on MSE equations, between the proposed estimators and the traditional combined and separate regression estimators. As a result of these comparisons, we observed that our proposed estimators give more efficient results than traditional approaches. And, these theoretical results are supported with the aid of numerical examples and simulation based on data sets that include outliers. [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]

In the present paper, an attempt has been made to develop two-steps calibration estimator of population mean in two-stage stratified random sampling, first by calibrating sampling design weight at psu level and second by calibrating stratum weight using known total of the auxiliary information at population level. A limited simulation study with real data has been conducted to examine the relative performance of the calibration estimators over the usual estimator of the population mean without using auxiliary information in two-stage stratified random sampling. Results of the simulation study show that two-steps calibration estimator has brought considerable improvement in the precision of the estimate of the population mean. [ABSTRACT FROM AUTHOR]

A new calibration estimator is proposed to estimate the population mean in the stratified random sampling. The corrected expression of Tracy et al. (2003) calibrated weights are presented and new improved calibration weights are introduced. Theoretical variance of the suggested estimator is discussed. Also a simulation study is carried out to show the properties of the proposed estimator. [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]

Grover and Kaur [4], Haq et al. [5], Abd-Elfattah et al. [1], Jhajj et al. [6], Shabbir and Gupta [8] and Koyuncu [7] have suggested some families of estimators by using the known population proportion of elements possessing attribute in the simple random sampling and in the two phase sampling. In this paper, after adapting these families of estimators to the stratified random sampling, we have proposed a family of exponential ratio type estimators which uses the information regarding the population proportion possessing certain attribute. For the proposed family of estimators, the expressions of bias and mean square error (MSE) up to the first order approximations are derived and the optimum case of the proposed family is discussed in theory. Also an empirical study is carried out to show its properties. [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]

Abstract: We propose some ratio-type variance estimators using ratio estimators for the population mean in literature. We obtain mean square error (MSE) equations of proposed estimators and show that proposed estimators are more efficient than the traditional ratio estimator, suggested by [C.T. Isaki, Variance estimation using auxiliary information, Journal of the American Statistical Association 78 (1983) 117–123], under certain conditions. We also adapt the proposed estimators in the simple random sampling to the stratified random sampling. In addition, we support the theoretical results with the aid of numerical examples. [Copyright &y& Elsevier]

Abstract: One of the most difficult problems confronting investigators who analyze data from surveys is how to treat missing data. Many statistical procedures cannot be used immediately if any values are missing. Imputation of missing data before starting statistical analysis is then necessary. This paper proposes imputation methods of the mean based on indirect estimators of available cases. A complete simulation study was performed to test the proposed techniques. [Copyright &y& Elsevier]