This paper addresses the problem of utilizing auxiliary information to estimate the population mean under neutrosophic stratified random sampling. Within the framework of neutrosophic statistics, we propose a neutrosophic combined logarithmic type estimator that effectively accounts for the uncertainty and indeterminacy inherent in survey sampling. The suggested estimator’s bias and mean squared error (MSE) are expressed up to the first order of approximation, and ideal circumstances for reducing the MSE are determined. Neutroposophic combined mean, neutrosophic combined ratio, and neutrosophic combined difference estimators are theoretically compared with the suggested estimator.The analytical findings show that in some real-world situations, the suggested estimator performs better than its conventional competitors. Additionally, line and radar chart representations and empirical data analysis are used to assess the performance of the suggested estimator. The results provide useful information for practitioners and survey statisticians who use Neutrosophic stratified random sampling when uncertainty is present.

In this article, an efficient class of product-type compromised methods of imputation has been considered, and their corresponding resultant point estimators have been suggested for missing data in survey sampling. This study gives a first-of-its-kind compromised imputation framework, offering improved efficiency over existing imputation methods. The bias and mean square error expressions for the suggested estimators have been developed. These estimators are more efficient than the mean method of imputation, Singh and Deo (2003) method of imputation, Shakti (2018) method of imputation, Prasad and Yadav (2023) methods of imputation, and adapted estimators for the method of imputation (Pandey and Dubey (1988), Bahl and Tuteja (1991), Upadhyaya and Singh (1999), Singh (2003), and Singh et al. (2004)). The numerical illustration and simulation studies illustrate that the proposed estimators are more efficient estimators than the existing estimators in this article.

In this article, we proposed a generalized data-driven class of exponential product-type imputation methods for handling missing data in survey sampling, and the corresponding point estimators are developed. We then derive the bias and mean square error expressions of the proposed estimators. The proposed estimators are more efficient estimators as compared to the existing estimators considered. A numerical illustration is carried out by considering varying population sample sizes ranging from 30 to 40

This study addresses variability in survey data by proposing an improved logarithmic cum exponential ratio class of estimators for estimating the population variance. The proposed estimators are constructed by combining logarithmic and exponential transformations within a power-adjusted ratio structure. Their statistical properties, including bias and mean squared error, are derived under a simple random sampling scheme without replacement. Optimality conditions are obtained, and theoretical efficiency comparisons are carried out against existing variance estimators. An empirical application using body mass index data, together with Monte Carlo simulations based on Gamma and Mixture-Gamma distributions, demonstrates the superior performance of the proposed estimator. The findings confirm that the suggested approach achieves higher precision and efficiency than competing estimators.

This paper proposes two innovative and efficient classes of estimators for estimating the population mean utilizing auxiliary information in simple random sampling. The bias and mean squared error of the proposed estimators are derived up to the first order of approximation under the simple random sampling without replacement scheme. The optimal conditions for minimizing the mean squared error of the newly developed estimators are determined. Efficiency conditions are derived by comparing the mean squared error of the proposed and existing estimators. To validate the study, an empirical analysis is conducted using four real population datasets, and a simulation study is conducted with 80,000 iterations. Based on the results from both the empirical and simulation studies, recommendations are made in favor of the suggested estimators.

This study develops two novel, efficient and consistent estimators for estimating the population mean of a study variable by incorporating auxiliary information under the Ranked Set Sampling framework. The expressions for bias and mean squared error are derived up to the first-order approximation. Conditions for which the proposed estimators outperform the usual unbiased estimator and other existing estimators are established in terms of efficiency. A Monte Carlo simulation study using synthetic datasets generated from both symmetric and asymmetric distributions and an empirical analysis based on two real datasets are conducted to evaluate the performance of the proposed estimators. The findings show that the suggested estimators perform better in terms of efficiency.

The present article deals with a generalized class of estimators for estimating the population mean in sample surveys, employing various combinations of auxiliary variables and considering some values of characterizing constant alpha ranging from -1 to +1. The proposed estimator may be consider as an efficient extension to the work of Singh and Shukla (Metron, 45(1-2): 273-283, 1987), Bahl and Tuteja (Journal of information and optimization sciences, 12(1), 159-164, 1991) and Kadilar (Journal of Modern Applied Statistical Methods: Vol. 15 : Iss. 2 , Article 15, 2016). The sampling properties of the suggested estimators have been derived up to the first degree of large sample approximations. The suggested estimators are shown to have smaller mean squared errors than the existing exponential estimators considered in this paper. The percent relative efficiencies with respect to the usual mean estimator are calculated. An improvement has been shown over the existing exponential estimators through theoretical conditions as well as by a numerical and simulation study based on COVID-19 death in India.

In the regression analysis, ordinary least squares techniques are commonly used. However, the data’s outcomes will be untrustworthy if there is an outlier in them. In order to deal with the outlier problem, various robust regression methods such as LTS, LMS, LAD, Huber-M, Hample-M, Tukey-M, and Huber-MM have been frequently presented as alternatives to OLS for a long time. In this article, primarily modified exponential ratio-type estimators based on OLS techniques are suggested. After that, robust regression method estimators are proposed, which is a useful strategy. The application of robust regression methods enhanced the efficiency of the estimators, especially for outliers in the data. The MSE equations of the various estimators are computed and compared to OLS approaches. Numerical illustration and simulation studies are performed on R program software to support our theoretical findings.

This paper addresses the problem of estimating the coefficient of variation of a study variable when the population coefficient of variation of an auxiliary variable is known, under a simple random sampling without replacement scheme. Five classes of estimators are proposed for estimating the coefficient of variation of the study variable using the known population coefficient of variation of the auxiliary variable. The biases and mean squared errors (MSEs) of the proposed classes of estimators are derived up to the first order of approximation. The efficiency of the proposed estimators is compared with conventional estimators such as the traditional estimator (which does not utilize auxiliary information), ratio-type estimator, product-type estimator, ratio-type exponential estimator, product-type exponential estimator, ratio and product-type logarithmic estimators and difference-type estimator. Conditions are obtained under which the proposed estimators outperform the conventional ones. A comprehensive numerical and simulation study is performed to assess the effectiveness and reliability of the proposed estimators. In addition, graphical illustrations are provided to compare the performance of the proposed estimators with existing ones.

This article deals with some new efficient linear regression ratio type estimators for estimating the population mean in sampling theory by using the auxiliary information of quartile deviation and deciles. The proposed estimators can be considered an efficient extension to the work of Kadilar and Cingi (Applied Mathematics and Computaton, 151, 893-902, 2004 \ Hacettepe Journal of Mathematics and Statistics, 35 (1), 103-109, 2006) and the Subjar (World Applied Sciences Journal, 35 (3), 377-384, 2017). The theoretical results are derived, and a comparative study is conducted. The suggested estimators are shown to have smaller mean squared errors than the Kadilar and Cingi (2004 \ 2006) and Subzar (2017) estimators. The percent relative efficiencies of the suggested estimators for various sample sizes are involved in simulation studies for a given natural population data set, and the results are found to be quite encouraging, providing an improvement over all previous work.

This paper presents a novel exponential cum logarithmic estimator designed to enhance the estimation of the population mean in simple random sampling, leveraging auxiliary attributes. The Mean Squared Error of the suggested estimator is derived up to the first-order approximation. The study identifies the optimal conditions under which the Mean Squared Error of the proposed estimator is minimized. The performance of the proposed estimator is compared with several established estimators in the literature as Naik and Gupta (1996) estimators, Singh et al. (2007) estimators, Abd-Elfattah (2010) estimators, Zaman and Kadilar (2019) estimator, Zaman (2020) estimator, Audu et al. (2022) estimators and Singh, Gupta and Tailor (2023) Estimators. Preference regions are established where the proposed estimator outperforms these alternative estimators. The proposed estimator's superior performance is validated through empirical studies using real-world data including the Health and Finance sectors, as well as simulation experiments through the Monte Carlo method using various probability distributions. Graphs are used to visualize the results from both the empirical and simulation analyses.

This study introduces three innovative hybrid efficient estimators designed for estimating population variance within the framework of simple random sampling. It highlights the critical role of auxiliary variables and their corresponding measures in the formulation of these hybrid estimators. We derive the bias and mean squared error of the proposed estimators, applying up to the first order of approximation. Additionally, we calculate the percent relative efficiency of these estimators and assess their performance against several established estimators using both gamma-simulated and real data sets. The results demonstrate that our proposed estimators typically achieve a lower mean squared error compared to the usual unbiased estimator and other existing estimators under specified conditions.

This article introduces some efficient generalized class of factor-type exponential imputation techniques and their corresponding estimators using auxiliary information. Generalized ratio, product, and dual to ratio type exponential estimators are the special cases of our suggested imputation techniques. Biases and mean squared error expressions are derived up to the first order of large sample approximations. The proposed imputation techniques can be viewed as efficient extensions of the work of Singh and Horn [Compromised imputation in survey sampling. Metrika. 2000;51(3):267-276. doi: 10.1007/s001840000054], Singh and Deo [Imputation by power transformation. Statist Papers. 2003;44(4):555-579. doi: 10.1007/BF02926010], Toutenburg and Srivastava [Amputation versus imputation of missing values through ratio method in sample surveys. Statist Papers. 2008;49(2):237-247. doi: 10.1007/s00362-006-0009-4], Kadilar and Cingi [Estimators for the population mean in the case of missing data. Commun Stat Theory Methods. 2008;37(14):2226-2236. doi: 10.1080/03610920701855020], Singh [A new method of imputation in survey sampling. Statistics. 2009;43(5):499-511. doi: 10.1080/02331880802605114], Gira [Estimation of population mean with a new imputation methods. Appl Math Sci. 2015;9(34):1663-1672] and Singh et al. [An improved alternative method of imputation for missing data in survey sampling. J Stat Appl Probab. 2022;11(2):535-543. doi: 10.18576/jsap]. Our proposed estimators are compared with these estimators, including the mean, ratio, and regression imputation techniques. Thereafter, a numerical illustration and simulation study are conducted for a comparative study using real and simulated data sets, and the demonstration shows that our suggested estimators are the most efficient estimators.

In regression analysis, the ordinary least squares (OLS) technique is a fundamental tool that is frequently used to model relationships between variables. If the data set contains an outlier, this technique could produce inaccurate outcomes. Outliers, or data points that are significantly different from the rest of the data, can lead to incorrect statistical interpretation. Systematically addressing outliers improves data precision and efficiency prior to drawing conclusions. Outliers may be misleading and cause statistical findings to be interpreted incorrectly. Prior to drawing conclusions from the data, it can be improved to acquirerelevant information by dealing with outliers effectively. The normality assumption is an essential statistical presumption that issusceptible to the effect of outliers in the context of traditional least squares regression. On the other hand, robust regression is a method used to estimate the regression coefficients in a linear regression model wherein the data contains outliers in an effort to improve the accuracy of the estimations. In the context of sample surveys, robust regression methods have been commonly suggested as useful alternatives to ordinary least squares for dealing with outliers. A novel exponential estimator for estimating the finite population mean is presented in this paper, which uses robust regression techniques while taking advantage of the known parameters of an auxiliary variable associated with the study variable. Assuming that the data contains outliers and that outliers have no effect on these estimators, as we are using the robust regression method, which is obtained by Huber M-estimation, our suggested estimators’ mean squared errors are determined theoretically and compared to the ordinary least squares techniques. The numerical illustration and simulation results demonstrate that, when using the robust regression method, the suggested estimators perform well in the presence of outliers.

In the regression analysis, ordinary least square techniques is commonly used. However, the data's outcomes may be untrustworthy if there is an outliers in it. In order to deal with the outliers problem, robust quantile regression methods have been frequently presented as alternatives to OLS for a long time. In this article, primarily a exponential ratio-type estimators is suggested. After that, robust quantile regression estimators are proposed, that is a useful strategy. The application of robust quantile regression empowered the efficiency of the estimators especially for outliers in the data. The MSE equations of the various estimators are computed and compared to OLS approaches. Numerical illustration and simulations studies are performed to support our theoretical findings.

In sample survey analysis, accurate population mean estimation is an important task, but traditional approaches frequently ignore the intricacies of real-world data, leading to biassed results. In order to handle uncertainties, indeterminacies, and ambiguity, this work presents an innovative approach based on neutrosophic statistics. We proposed novel neutrosophic factor type exponential estimators that use auxiliary information to improve population mean precision. The suggested estimators are highly useful for computing results while working with unclear, hazy, and neutrosophic-type data. These estimators produce answers that are interval-form rather than single-valued, which may give our population parameter a better chance of being off. Since we now have an estimated interval with the population mean's unknown value supplied a minimal MSE or highest Percentage Relative Efficiency (PRE), the estimators are more effective. The investigation of alpha constants in the neutrosophic framework, extending from -1 to +1, is at the heart of this research. These constants have a significant impact on how estimates are made and enable flexible accuracy modification. We choose the best neutrosophic values for characterizing constants, emphasizing their importance in obtaining precise estimations. This study further expands its originality by including preexisting estimators into the neutrosophic framework, showcasing its versatility and adaptability. We demonstrate the estimators' superiority to traditional techniques through empirical assessments employing neutrosophic temperature information and simulation assessments. The ensuing interval-based results, influenced by alpha constants, offer concrete insights into the world of uncertainty, enabling more well-informed decision-making.

The current article discusses ratio type exponential estimators for estimating the mean of a finite population in sample surveys.The estimators uses robust regression's Huber M-estimation function, and their bias as well as mean squared error expressions are derived.It was campared with Kadilar, Candan, and Cingi (Hacet J Math Stat, 36, 181-188, 2007) estimators.The circumstances under which the suggested estimators perform better than competing estimators are discussed.Five different population datasets with a well recognized outlier have been widely used in numerical and simulation-based research.These thorough studies seek to provide strong proof to back up our claims by carefully assessing and validating the theoretical results reported in our study.The estimators that have been proposed are intended to significantly improve both the efficiency and accuracy of estimating the mean of a finite population.As a result, the results that are obtained from statistical analyses will be more reliable and precise.

In the context of sample surveys, this article presents factor-type exponential ratio estimators as a technique for estimating population means. These estimators use supplementary information for an auxiliary variable, such as coefficients of variation and coefficients of kurtosis. The research includes a range of alpha values from −1 to +1 to improve estimate.

Point estimates in survey sampling only provide a single value for the parameter being studied and are consequently vulnerable to changes caused by sampling error. In order to cope with ambiguity, indeterminacy, and uncertainty in data, Florentin Smarandache’s neutrosophic technique, which generates interval estimates with high probability, offers a helpful solution. To estimate the neutrosophic population mean of the studied variable, this research provides new neutrosophic factor type exponential estimators using well-known neutrosophic auxiliary parameters. For the first-degree of approximation, the study derives the bias and Mean Squared Error (MSE) of the proposed estimators. Characterising constants have neutrosophic optimal values, and for these optimum values, the least value of the neutrosophic MSE is obtained. Notably, the proposed neutrosophic estimators outperform the corresponding adapted classical estimators since their estimated interval falls under the minimal MSE and lies within the estimated interval of the proposed neutrosophic estimators. The theoretical results are supported by empirical data from real data sets acquired by the “Ministry of Earth Sciences” and the “India Meteorological Department (IMD), Pune, India,” as well as simulated data sets produced via Neutrosophic Normal Distribution. The estimator with the lowest MSE is suggested for practical applications across many domains, providing greater accuracy and reliability in parameter estimation when utilising the neutrosophic methodology.

Some efficient product type exponential imputation methods are proposed in this article to tackle the problem of incomplete values in sampling theory. To investigate the effectiveness of proposed exponential methods, the behaviours of the considered estimators are compared in two scenarios: with and without nonresponse. The simulation studies show that the proposed resultant estimators outperform other existing estimators in this literature.