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.

Ayaydın, Hasan; Shırzadı, Said Abdullah; Balıkçı, Hasan Celal; Sharabati, Abdel-Aziz Ahmad; Barut, Abdulkadir

Assessing the financial performance of an enterprise is crucial for demonstrating the effectiveness of the management team, influencing investor decision-making regarding stocks, and meeting the expectations of lending institutions. Cement companies prioritise strengthening their capital structures to better withstand economic shocks. Additionally, these companies aim to use their resources more efficiently to reduce costs, increase sales, boost profits, and achieve faster growth. Accurate evaluations of the financial performance of cement companies are crucial for both the global economic landscape and their impact on the local economy. Consequently, financial performance evaluation models are essential for these companies to enhance their long-term financial results. However, to date, there appears to be no existing model that suggests periodic monitoring of the financial performance of cement companies based on their financial ratios. The current study seeks to analyze the TOPSIS and VIKOR multi-criteria decision-making methods, utilizing an entropy approach among publicly traded cement sector companies based on BIST, data between 2018 and 2020. The objective is to disclose the results of these companies and rank them based on key criteria. Additionally, a comparative analysis was conducted using the VIKOR method. The results indicated that the rankings produced by the TOPSIS and VIKOR methods were similar for 2020 and 2021, though they differed slightly in 2022 and 2023. Generally, BSOKE, KONYA, and BTCIM ranked as the top three companies in both methods, while CIMSA, BASCM, and CMENT ranked the lowest. Furthermore, companies such as AKCNS, CMENT, BUCIM, GOLTS, and NUHCM showed fluctuations in their performance. The study discusses managerial implications and provides recommendations for stakeholders.

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.

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.

Soydaş, Şafak Sönmez; Kalkan, Yusuf; Çam, Alper Veli; Barut, Abdulkadir

Recently, machine learning (ML) algorithms have been employed intensively in the field of finance as in all sectors. The issues such as financial distress prediction, bank credit risk calculation, etc., have been analyzed using ML algorithms. This study aimed to determine firm performance with the data envelopment analysis (DEA) method, sensitivity analysis, and ML algorithms and analyze the efficiency of companies via artificial neural networks (ANNs), support vector machines (SVMs), and logistic regression (LR) classification algorithms. In the study, first, 10 financial ratios were categorized into two parts, such as output and input, and efficiency scores were determined in MS Excel software. The obtained scores were included in the ML algorithm as a categorical dependent variable. Secondly, the data were extracted and included in the analysis software as 80% training and 20% test data, and the accuracy of ML algorithms was tested. Lastly, a comparative analysis of the estimation and classification algorithms of active and inactive companies was conducted. As a result of the analysis, the best classification prediction was seen as the ANN algorithm. SVM and LR algorithms also made an acceptable level of classification prediction. It was expected that the study would have contributed to the literature in terms of testing the companies whose efficiency scores were determined by the DEA method with ML techniques and determining which technique was more successful.