The traditional stratification weight is widely used in survey sampling for estimation under stratified random sampling (StRS). A neutrosophic calibration approach is proposed under neutrosophic statistics for the first time with the aim of improving conventional stratification weight. This addresses the challenge of estimating the empirical cumulative distribution function (CDF) of a finite population using the neutrosophic technique. The neutrosophic technique extends traditional statistics, dealing with indeterminate, vague, and uncertain values. Thus, using additional information, we are able to obtain an effective estimate of the neutrosophic CDF. The suggested estimator yields an interval range in which the population empirical CDF is likely to exist rather than a single numerical value. The proposed family of neutrosophic estimators will be defined under suitable calibration constraints. A simulation study is also computed in order to assess the effectiveness of the suggested and adapted neutrosophic estimators using real-life symmetric and asymmetric datasets.

To improve the effectiveness of population estimators, researchers have recently implemented dual supplementary information. They employed traditional rankings, the empirical cumulative distribution function, and indicator functions as supplementary sources of information in their analysis. An improved family of population mean estimators is introduced in this article, which utilizes the relative ranks of the auxiliary information’s configurations to incorporate the relevant information. A first-order approximation is employed to derive the mathematical expressions for the bias and the mean-squared error (MSE) of the proposed family of estimators. The empirical analysis is investigated to demonstrate the practicality of the proposed estimators in real-world scenarios. Additionally, the theoretical conclusions are effectively validated by the Monte Carlo simulation integration. Our results unequivocally indicate that the proposed family of estimators surpasses their current counterparts.

This research analyzes the marine route plan from Shanghai to Vladivostok utilizing Dijkstra’s algorithm, Markov chain analysis, game theory, and congestion analysis. Dijkstra determines the route through Busan and Hungnam as the shortest, with a total distance of 2114 kilometers and minimal travel time. The Markov chain analysis supported the designated path by demonstrating greater transition probabilities compared to other routes, so establishing it as the most probable option. Experts in game theory, particularly on the Nash equilibrium, demonstrated that cooperation significantly reduced operating expenses. Further congestion research corroborated that the Shanghai-Busan-Hungnam-Vladivostok route offers a cost advantage, and even with the inclusion of congestion, the route remains less expensive. The study collectively advocates for the consideration of distance, likelihood, collaboration, and congestion while selecting the optimal maritime route, hence enhancing efficiency in maritime logistics.

In modern survey sampling, particularly when using stratified random sampling (StRS), the existence of outliers and model mis-specifications is a daunting challenge to the conventional parametric and nonparametric methods of estimating parameters. This research presents a new type of predictive estimator that is synergistic to both robust regression and nonparametric local polynomial kernel regression. It aims to offer more resistant and efficient estimators of the average parameter in the areas where supplementary information is known, but irregularity in the data is usual. The proposed estimators use dual calibration methods based on both auxiliary variable means and coefficients of variation, which improves efficiency. This framework enhances predictive performance by integrating the adaptability of kernel-based smoothing with the outlier resistance of robust regression. The accuracy of the suggested estimators is measured by using large scales of simulation experiments on artificial populations with structural heterogeneity and outlier contamination. An empirical comparison, based on percentage relative efficiency (PRE), indicates that the new estimators are superior to classical methods based on the use of a kernel regression in most bandwidth selection strategies. In addition to bringing methodological innovation as it connects distribution theory, regression models, and robust estimation strategies, this work also offers the usefulness of survey practitioners who work with complicated and imperfect real-life data of fisheries and radiations.

This study offers a comparative analysis of parametric, semi-parametric, and non-parametric techniques of survival data analysis. It focuses on assessing the methods in terms of their efficiency in estimating survival probabilities and hazard functions. Alternate real and simulated datasets are then utilized to assess the relative strengths and weaknesses of different approaches with respect to efficiency, flexibility, and interpretability. The results indicate that the choice of technique is a factor of the underlying data characteristics, with parametric models working quite well under specific assumptions, semi parametric methods balancing between the structure and flexibility, while the nonparametric ones have the best performance in scenarios driven by the data. The AIC and BIC values determined from the model selection process demonstrated that the model finding fits the best under the given conditions as it has the smallest AIC and BIC scores. This is because AIC and BIC balance between goodness of fit and complexity in the model. For the Cox proportional hazard model results, most variables tested in the analysis showed very few statistically significant p- values. Results from Kaplan-Meier survival analysis strengthened the model by providing critical survival probability key stages over time, indicating the most occurrences beyond 60 months marked significant declines in survival rates. Hence, since the determined model com- bines predictive accuracy with interpret-ability, it should have as few but robust elements as would be meaningful in survival prediction without over-fitting. Therefore, the final model selection is based on the best compromise between explanatory power and statistical validity.