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This article addresses the problem of estimating the population distribution function for stratified random sampling in the presence of non-response. We suggest a generalized class of estimators for estimating the finite cumulative distribution function using the auxiliary information. Expressions for bias and mean squared error are derived up to the first order of approximation. The performance of the estimators are compared both theoretically and numerically. A real data set is used to support the theoretical findings. [ABSTRACT FROM AUTHOR]

In this research, a logarithmic-type estimator was formulated for estimating the finite population variance in stratified random sampling. By ensuring that the sampling process is symmetrically conducted across the population, biases can be minimized, and the sample is more likely to be representative of the population as a whole. We conducted a comprehensive numerical study and simulation study to evaluate the performance of the proposed estimator. The mean squared error values were computed for both our proposed estimator and several existing ones, including the standard unbiased variance estimator, difference-type estimator, and other considered estimators. The results of the numerical study and simulation study demonstrated that the proposed log-type estimator outperforms the other considered estimators in terms of MSE and percentage relative efficiency. Graphical representations of the results are also provided to illustrate the efficiency of the proposed estimator. Based on the findings of this study, we conclude that the proposed log-type estimator is a valuable addition to the existing literature on variance estimation in stratified random sampling. It provides a more efficient and accurate estimate of the population variance, which can be beneficial for various statistical applications. [ABSTRACT FROM AUTHOR]

Measurement error, which occurs when some variables of interest cannot be observed exactly. It often leads to invalid conclusions and lamentable implications if ignored. Therefore, the aim of the present work is to investigate ways to handle them when the observations on both the study and auxiliary variables are spoiled with measurement errors. A general class of estimators for the estimation of population mean in presence of correlated measurement errors on study and auxiliary variables has been considered. The variations in the properties of the estimators under the influence of measurement errors has been discussed in detail. The considered class of estimators is compared with the recently proposed family of exponential-type estimators. Simulation studies are carried out to show the impact on the behavior of estimators due to variation in parameters of the measurement error model. [ABSTRACT FROM AUTHOR]

In this paper, a new scrambled randomized response (SRR) model has been proposed for estimating the population mean of a sensitive variable in presence of scrambled response under simple random sampling with replacement (SRSWR). The utility of proposed SRR model under two occasions successive sampling is also explored. It is found that the proposed SRR model is superior to the additive model of Gjestvang and Singh (2009) under SRSWR and successive sampling. We also proposed a composite class of estimators for estimating the population mean of sensitive variable under two occasions successive sampling. The proposed composite class of estimators under optimum conditions is shown to be more efficient than the classical ratio and exponential ratio type estimators respectively. [ABSTRACT FROM AUTHOR]

In this article, maximum likelihood estimation and Bayes estimators are derived for linear exponential distribution based on one- and m-cycle extreme ranked set sampling and simple random sample. These estimators are compared via their biases and mean squared error. This is done with respect to both symmetric and asymmetric loss functions. A reallife data set and simulation study are used to illustrate our procedures. This unequivocally shows that, in all circumstances taken into consideration for this study, ranked set sampling is more effective than both extreme ranked set sampling and simple random sample. Over a high number of cycles, a significant improvement is shown. [ABSTRACT FROM AUTHOR]

The empirical best linear unbiased predictor (EBLUP) is a linear shrinkage of the direct estimate toward the regression estimate and useful for the small area estimation in the sense of increasing precision of estimation of small area means. However, one potential difficulty of EBLUP is that the overall estimate for a larger geographical area based on a sum of EBLUP is not necessarily identical to the corresponding direct estimate like the overall sample mean. To fix this problem, the paper suggests a new method for benchmarking EBLUP in the Fay–Herriot model without assuming normality of random effects and sampling errors. The resulting benchmarked empirical linear shrinkage (BELS) predictor has novelty in the sense that coefficients for benchmarking are adjusted based on the data from each area. To measure the uncertainty of BELS, the second‐order unbiased estimator of the mean squared error is derived. [ABSTRACT FROM AUTHOR]

Ranked set sampling is an approach to data collection originally combines simple random sampling with the field investigator's professional knowledge and judgment to pick places to collect samples. Alternatively, field screening measurements can replace professional judgment when appropriate and analysis that continues to stimulate substantial methodological research. The use of ranked set sampling increases the chance that the collected samples will yield representative measurements. This results in better estimates of the mean as well as improved performance of many statistical procedures. Moreover, ranked set sampling can be more cost-efficient than simple random sampling because fewer samples need to be collected and measured. The use of professional judgment in the process of selecting sampling locations is a powerful incentive to use ranked set sampling. This paper is devoted to the study, we introduce an approach to the mean estimators in ranked set sampling. The amount of information carried by the auxiliary variable is measured with the on populations and samples and to use this information in the estimator, the basic ratio and the generalized exponential ratio estimators are as an improved form of a difference cum exponential ratio type estimator under the ranked set sampling in order to estimate the population mean Y of study variate Y using single auxiliary variable X. The expressions for the mean squared error of propose estimator under ranked set sampling is derived and theoretical comparisons are made with competing estimators. We show that the proposed estimator has a lower mean square error than the existing estimators. In addition, these theoretical results are supported with the aid of some real data sets using R studio. Therefore, Under RSS architecture, a better difference cum exponential ratio type estimator has been suggested. The estimator's mathematical form has been developed, and its efficiency requirements have been developed in relation to various already-existing estimators from the literature. By imputing various values for the constants used in the creation of our proposed estimator, we also provide several specific situations of our estimator. [ABSTRACT FROM AUTHOR]

We propose a novel family of estimators for the population mean under non-response and obtain the MSE equation of the suggested estimator for each situation in theory. These theoretical conditions are applied to three popular data sets in literature and we see that the suggested estimators are more efficient than the traditional estimators, such as ratio, regression estimators, in Case 1; whereas, in Case 2, the suggested estimators are also more efficient than the Unal-Kadilar exponential estimators that are more efficient than the traditional estimators for the same data sets. [ABSTRACT FROM AUTHOR]

In the present paper, a general class of estimators has been proposed for estimating the population mean of the study variable using auxiliary variable when the population mean of the auxiliary variable is known in stratified random sampling. The bias and mean squared error of the proposed class of estimators are derived under stratified sampling to the first degree of approximation. Members of the proposed class of estimators are also obtained by giving suitable values to the constants used. Comparisons of the proposed strategy with the usual unbiased estimator and other estimators have been made. Empirical and simulation study is carried out to demonstrate the performance of the proposed estimator. [ABSTRACT FROM AUTHOR]

This article is about making correction in Tarray, Singh, and Zaizai model and further improving it when stratified random sampling is necessary. This is done by using optional randomized response technique in stratified sampling using a combination of Mangat and Singh, Mangat, and Greenberg et al. models. The suggested model has been studied assuming proportional and Neyman allocation schemes. Numerical results show larger gains in efficiency. Through a detailed numerical study, it is established that the suggested model is relatively more efficient than the Kim and Warde model and the models mentioned above. [ABSTRACT FROM AUTHOR]

In this manuscript, we study the problem of separate type exponential ratio estimator for estimation of population mean with their properties. The MSE and Bias up to the first degree of approximation for the suggested estimator are computed. The suggested estimator is proven to be more efficient than estimators mentioned in the literature under stratified random technique. An empirical investigation was done to assess the suggested estimator. Also, the percent relative efficiency is to be remarkable for the proposed estimator. [ABSTRACT FROM AUTHOR]

In this study, the difficulty of estimating the population mean in the situation of post-stratification is discussed. The case of post-stratification is presented for ratio-type exponential estimators of finite population mean. Mean-squared error of the proposed estimator is obtained up to the first degree of approximation. In the instance of post-stratification, the proposed estimator was compared with the existing estimators. An empirical study by using some real data and further, simulation study has been carried out to demonstrate the performance of the proposed estimator. [ABSTRACT FROM AUTHOR]

In this article, we first discuss a few properties along with limitations of traditional measure of coefficient of dispersion in comparison to the well-known standard measure of variation called the coefficient of variation. To overcome the limitations in the traditional coefficient of dispersion, a new measure of coefficient of dispersion is introduced which is more informative than the conventional one. A new naïve estimator of the newly developed measure of coefficient of dispersion is proposed. The bias and variance expressions for the naïve estimator are derived to the first order of approximation. In the presence of an auxiliary variable, ratio and regression type estimators for estimating the new measure of coefficient of dispersion are also proposed. The bias and the variance expressions to the first order of approximation are derived. A simulation study, using R language, to judge the performance of the proposed ratio and regression type estimators with respect to the naïve estimator is considered. At the end, applications of the proposed ratio and regression type estimators based on real data sets are discussed. [ABSTRACT FROM AUTHOR]