Yazarlar
Yayınlar
- MakaleYalnız künye2026A novel neutrosophic estimation method for handling uncertainty in population mean estimation
Yadav, Akhilesh; Yadav, Dharmendra Kumar; Yadav, Subhash Kumar; Zaman, Tolga
Estimating the population mean using auxiliary information has been extensively explored within the classical framework. While point estimators offer simplicity, they provide only a single value without reflecting the uncertainty or variability inherent in real-world data. This shortcoming is particularly critical in high-precision applications and decision-making contexts. Moreover, classical estimators are often vulnerable to the influence of outliers, which can distort results and introduce significant bias. To address these limitations, this study introduces a novel estimator grounded in neutrosophic theory, which is specifically designed to handle imprecise, vague, and incomplete information-conditions frequently encountered in practical sampling scenarios. The proposed neutrosophic estimator incorporates auxiliary information expressed in neutrosophic terms, offering a more flexible and resilient estimation strategy. We derive expressions for the bias and mean square error of the proposed estimator under a first-order approximation. A comprehensive theoretical analysis, supported by simulation studies, confirms that the neutrosophic estimator consistently outperforms classical estimators in terms of accuracy and robustness, especially in uncertain environments. These findings underscore the potential of neutrosophic approaches as powerful alternatives to conventional estimation techniques in modern statistical inference.
- MakaleYalnız künye2026Quantile-based neutrosophic regression estimators under stratified random sampling
Zaman, Tolga; Sozen, Caglar
The neutrosophic framework provides a convenient way to represent uncertainty and indeterminacy through interval-valued quantities. This paper proposes quantile-based neutrosophic combined and separate regression estimators for the finite population mean under stratified random sampling. Building on the combined and separate quantile regression estimators of Koç and Koç (Axioms 12:713, 2023), we incorporate neutrosophic (interval) versions of stratum-specific quantile regression components and derive approximate bias and mean squared error (MSE) expressions using first-order linearization. We provide efficiency conditions under which the proposed neutrosophic estimators improve upon their classical counterparts and we evaluate performance via (i) a simulation study comparing classical and neutrosophic estimators, and (ii) a real-data illustration based on monthly high–low index data (2010–2024) for the S&P 500, Nikkei 225, and DAX, where MSE bounds are reported for the proposed neutrosophic estimators. Across the considered experiments, the neutrosophic separate estimator at the 0.50 quantile attains the smallest MSE among the reported competitors, suggesting that a median-oriented robust component combined with stratification can be effective when observations are imprecise. Limitations and directions for further extensions are discussed.
- MakaleYalnız künye2026A logarithmic-type estimator for finite population variance and its efficiency properties
Iftikhar, Soofia; Alamgir; Zaman, Tolga
This study proposes a novel logarithmic-type estimator for the finite population variance. We derive the mean square error (MSE) expressions, approximated to the first order, along with the associated efficiency conditions for the proposed estimator, as well as for several existing estimators from the literature. A comprehensive simulation study is conducted to compare the efficiency of the new estimator against these established methods. Based on the analysis of a real dataset, the results demonstrate that the proposed estimator achieves the minimum mean square error and satisfies all relevant efficiency criteria. Consequently, it is concluded that the proposed estimator outperforms the other considered estimators in terms of efficiency.
- MakaleYalnız künye2026Improved generalized ratio estimators of population mean using auxiliary variables
Yadav, Subhash Kumar; Arya, Diksha; Sharma, Prayas; Zaman, Tolga
The Quest for the improved estimators of population mean is a continuous process to estimate it more efficiently and more closely to the true population mean of the study variable. There are various estimators of population mean in the literature, which estimate it efficiently, but still there is space for more efficient estimators. For improved estimation of population mean of primary variable, we suggest six different searls type ratio estimators utilizing the known auxiliary parameters. We derive the expressions for the biases and the mean squared errors of the introduced estimators for an approximation of degree one. The optimal values of the Searls characterizing constants, which minimize the mean squared errors of the introduced estimators, are obtained. The least value of the mean squared error of the proposed estimators for these optimal values of the characterizing scalars is also acquired. The efficiency of the proposed estimators is compared theoretically with the competing estimators of population mean through their mean squared errors. We derive the efficiency conditions of the proposed estimator for which it is more efficient than the estimators of population mean in competition. These efficiency conditions of the proposed estimators are verified through empirical data and the improvement over the competing estimators is calculated based on the minimum values of the mean squared errors. It is evident from the numerical study that the proposed estimator has the least mean squared error and the highest percentage relative efficiency in comparison to competing estimators. Thus, the proposed estimators are recommended for applications in different areas of applications.
- MakaleAçık Erişim2025A new class of robust ratio estimators for finite population variance
Zaman, Tolga; Bulut, Hasan
It is a general practice to use robust estimates to improve ratio estimators using functions of the parameters of an auxiliary variable. In this study, a new class of robust estimators based upon the Minimum Covariance Determinant (MCD) and the Minimum Volume Ellipsoid (MVE) robust covariance estimates have been suggested for estimating population variance in the presence of outlier values in the data set for the simple random sampling. The expression for the Mean Square Error (MSE) of the proposed class of estimators is derived from the first degree of approximation. The efficiency of the proposed class of robust estimators is compared with some competing estimators discussed in the literature, and found that proposed estimators are better than other mentioned estimators here. In addition, real data set and simulation studies are performed to present the efficiencies of the estimators. We demonstrate theoretically and numerically that the proposed class of estimators performs better than all other competitor estimators under all situations.
- MakaleAçık Erişim2025Neutrosophic exponential estimators for estimating the population mean using auxiliary attributes: An application to temperature data
Zaman, Tolga; Sozen, Caglar
Classical statistical methods cannot directly incorporate measurement uncertainties and indeterministic elements into the model, leading to estimation errors and biases, especially in environmental datasets. Neutrosophic statistics, on the other hand, offers a broader approach that does not only rely on exact values, but also includes ambiguous, incomplete and conflicting information. In this study, we propose a new neutrosophic ratio-type estimator for estimating the population mean using monthly temperature data for Turkey for the period 2010-2022. This study proposes neutrosophic multivariate exponential estimators considering two auxiliary attributes. The mean square error equations of all proposed neutrosophic exponential estimators are obtained theoretically. Some conditions of neutrosophic multivariate exponential estimators are considered and the properties of these estimators are studied. The effectiveness of the proposed neutrosophic exponential estimators is analyzed using neutrosophic temperature data. In addition to real data analysis, extensive simulation studies are conducted under various scenarios to evaluate the performance of the estimators. The results demonstrate that the proposed estimators perform effectively under uncertainty and can be successfully applied in forecasting environmental variables.
- MakaleAçık Erişim2024A NEW LOGARITHMIC TYPE ESTIMATORS FOR ANALYSIS OF NUMBER OF AFTERSHOCKS USING POISSON DISTRIBUTION
Zaman, Tolga; Iftikhar, Soofia; Sozen, Caglar; Sharma, Prayas
Earthquake is a fierce and unavoidable natural disaster. The study of aftershocks following an earthquake provides a comprehensive understanding of seismic activity, making it an important area of research. Although it is important to mention that the use of the Poisson distributed population, to estimate the mass mean of rare events such as earthquakes, has been little studied in the literature. In this context, this study proposes a new class of logarithmic type ratio estimators for estimating the mean of a Poisson distributed population in a simple random sampling without replacement. The proposed estimators are obtained using the logarithmic transformation of the ratio estimator, and the expressions for the mean square error (MSE) are also derived to the first order of approximation. The study demonstrates that the proposed logarithmic type estimators are more efficient than the existing estimators, both theoretically and empirically. The Empirical evidence from a real data study conducted using earthquake data from Turkey confirms the superiority of the proposed estimators over the existing estimators. The study provides valuable insights for researchers and practitioners working with Poisson distributed populations and requiring ratio estimators using simple random sampling without replacement.
- MakaleAçık Erişim2024AN EFFICIENT FAMILY OF RATIO TYPE ESTIMATORS FOR SIMPLE RANDOM SAMPLING
Yadav, Subhash Kumar; Arya, Diksha; Koc, Tuba; Zaman, Tolga
The study provides an enhanced estimation of the population mean using known information on an auxiliary variable. An enhanced class of estimators is suggested for the same. The proposed estimator's bias and mean squared error (MSE) are calculated up to the first order of approximation. The optimum values of the characterizing constants are obtained by minimizing the MSE of the proposed estimator. The minimum MSE and the bias values are achieved by optimising the characterizing scalar. The MSE of the proposed estimator has also been compared both conceptually and empirically with the MSEs of competing estimators. Real and simulated data sets are adopted to verify the theoretical prerequisites for the proposed estimator's greater efficiency over competing estimators. The most efficient estimator is recommended for practical utility in different areas of applications and the suggested estimator filfills the requirement.
- MakaleAçık Erişim2024Improved estimation of population variance in stratified successive sampling using calibrated weights under non-response
Pandey, M. K.; Singh, G. N.; Zaman, Tolga; Al Mutairi, Aned; Mustafa, Manahil SidAhmed
This paper introduces a new method to estimate the population variance of a study variable in stratified successive sampling over two occasions, while accounting for random non -response. The method uses a logarithmic type estimator and leverages information from a highly positively correlated auxiliary variable. The paper also presents calibrated weights for the new estimator and examines its properties through numerical and simulation studies. The results indicate that the suggested estimator is more effective than the standard estimator for estimating the population variance.
- MakaleAçık Erişim2023A NEW LOGARITHMIC RATIO TYPE ESTIMATOR OF POPULATION MEAN FOR SIMPLE RANDOM SAMPLING: A SIMULATION STUDY
Zaman, Tolga; Iftikhar, Soofia
This paper provides a logarithmic ratio type estimator of a finite population mean using an auxiliary variable in simple random sampling. The expression for the mean square error (MSE) of the proposed estimator and some selected estimators are calculated along with the theoretical conditions. Further the numerical comparisons and simulation study based on different probability distributions supports the claim that the proposed estimator performs better as compared to the ones used in this study.