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Researchers have devoted decades to striving to create a plethora of distinctive distributions in order to meet specific objectives. The argument is that traditional distributions have typically been found to lack fit in real-world situations, which include pharmaceutical studies, the field of engineering, hydrology, environmental science, and a number of others. The Weibull-inverse Nadarajah Haghighi (WINH) distribution is developed by combining the Weibull and inverse Nadarajah Haghighi distributions. The proposed distribution's fundamental characteristics have been established and analyzed. Several plots of the distributional properties, notably probability density function (PDF) with corresponding cumulative distribution function (CDF) are displayed. The estimation of model parameter is performed via the MLE procedure. Simulation-based research is conducted to demonstrate the performance of proposed estimator’s using some measure, like the average bias, variance, and associated mean square error (MSE). Two real datasets represent the morality due to COVID 19 in France and Canada are illustrated to see the practicality of the recommended model.

Researchers have devoted decades to striving to create a plethora of distinctive distributions in order to meet specific objectives. The argument is that traditional distributions have typically been found to lack fit in real-world situations, which include pharmaceutical studies, the field of engineering, hydrology, environmental science, and a number of others. The Weibull-inverse Nadarajah Haghighi (WINH) distribution is developed by combining the Weibull and inverse Nadarajah Haghighi distributions. The proposed distribution's fundamental characteristics have been established and analyzed. Several plots of the distributional properties, notably probability density function (PDF) with corresponding cumulative distribution function (CDF) are displayed. The estimation of model parameter is performed via the MLE procedure. Simulation-based research is conducted to demonstrate the performance of proposed estimator's using some measure, like the average bias, variance, and associated mean square error (MSE). Two real datasets represent the morality due to COVID 19 in France and Canada are illustrated to see the practicality of the recommended model. [ABSTRACT FROM AUTHOR]

In this article, we present a comprehensive class of estimators designed for population proportion estimation by leveraging auxiliary attributes within the framework of simple random sampling. The proposed class encompasses a diverse range of estimators, each of which undergoes a thorough examination. We provide numerical expressions for both bias and mean squared error, employing a first-order approximation. The significance of the introduced class of estimators is underscored through a detailed analysis of numerical results. These findings demonstrate the marked superiority of the suggested estimators over their existing counterparts in terms of mean squared error and percentage relative efficiency, as observed in both actual and simulated data scenarios. Consequently, we advocate for the adoption of the proposed class of estimators, asserting its potential to yield improved outcomes when estimating population proportions through the utilization of simple random sampling techniques.

In this article, we present a comprehensive class of estimators designed for population proportion estimation by leveraging auxiliary attributes within the framework of simple random sampling. The proposed class encompasses a diverse range of estimators, each of which undergoes a thorough examination. We provide numerical expressions for both bias and mean squared error, employing a first-order approximation. The significance of the introduced class of estimators is underscored through a detailed analysis of numerical results. These findings demonstrate the marked superiority of the suggested estimators over their existing counterparts in terms of mean squared error and percentage relative efficiency, as observed in both actual and simulated data scenarios. Consequently, we advocate for the adoption of the proposed class of estimators, asserting its potential to yield improved outcomes when estimating population proportions through the utilization of simple random sampling techniques. [ABSTRACT FROM AUTHOR]

The major goal of this research study is to solve the fractional order Wolbachia invasive model (FWIM) by developing a computational framework based on the Bayesian regularization backpropagation neural network (BRB-NN) approach. The population of mosquitoes is categorized into two classes, Wolbachia-infected mosquitoes and Wolbachia-uninfected mosquitoes. We also incorporate incomplete cytoplasmic incompatibility and imperfect maternal transmission. We investigate the effects of the fractional order derivative (α) and reproduction rate of Wolbachia-infected mosquitoes ϕw on the dynamics of mosquitoes. The proposed Bayesian regularization backpropagation scheme is applied to three distinct cases using 80% and 20% of the created dataset for training and testing, respectively, with 15 hidden neurons. Comparisons of the results are presented to verify the validity of the proposed technique for solving the model. The Bayesian regularization approach is used to lower the mean square error (MSE) for the fractional order Wolbachia invasive model. The achieved results are based on MSE, correlation, state transitions, error histograms, and regression analysis to confirm the effectiveness of the suggested approach. Additionally, the absolute error value modifies the designed approach’s accuracy.

The major goal of this research study is to solve the fractional order Wolbachia invasive model (FWIM) by developing a computational framework based on the Bayesian regularization backpropagation neural network (BRB-NN) approach. The population of mosquitoes is categorized into two classes, Wolbachia-infected mosquitoes and Wolbachia-uninfected mosquitoes. We also incorporate incomplete cytoplasmic incompatibility and imperfect maternal transmission. We investigate the effects of the fractional order derivative (α) and reproduction rate of Wolbachia-infected mosquitoes ϕ w on the dynamics of mosquitoes. The proposed Bayesian regularization backpropagation scheme is applied to three distinct cases using 80% and 20% of the created dataset for training and testing, respectively, with 15 hidden neurons. Comparisons of the results are presented to verify the validity of the proposed technique for solving the model. The Bayesian regularization approach is used to lower the mean square error (MSE) for the fractional order Wolbachia invasive model. The achieved results are based on MSE, correlation, state transitions, error histograms, and regression analysis to confirm the effectiveness of the suggested approach. Additionally, the absolute error value modifies the designed approach's accuracy. [ABSTRACT FROM AUTHOR]

In this article, we offer simple random sampling (SRS) based efficient class of estimators of population mean Y¯ utilizing additional information. The expression of the mean square error of the proposed class of estimators is deduced up to first degree approximation. The efficiency conditions are established which are enhanced numerically utilizing a simulation study consummated over symmetrical and asymmetrical populations. Real data sets are also utilized to exemplify the suggested estimators. The numerical findings are appeared rather acceptable demonstrating better advancement over the ordinary estimators.

In the sample survey theory, the crux of survey practitioners is to provide “accurate” estimators of the parameter of choice. The conventional theory depends on the regression/difference estimators as they correspond to the best linear unbiased (BLU) estimators. This paper suggests some optimal classes of estimators by modifying the conventional estimators under stratified ranked set sampling (SRSS). The characteristics of the suggested estimators are established to the first-order approximation. The performance of the suggested class of estimators under SRSS has been theoretically and experimentally shown to be superior to traditional estimators, particularly regression (BLU) estimators.

This article presents a useful application of the Manta Ray Foraging Optimization (MRFO) algorithm for solving the adaptive infinite impulse response (IIR) system identification problem. The effectiveness of the proposed technique is validated on four benchmark IIR models for reduced order system identification. The stability of the proposed estimated IIR system is assured by incorporating a pole-finding and initialization routine in the search procedure of the MRFO algorithm and this algorithmic modification contributes to the MRFO algorithm when seeking stable IIR filter solutions. The absence of such a scheme, which is primarily the case with the majority of the recently published literature, may lead to the generation of an unstable IIR filter for unknown real-world instances (particularly when the estimation order increases). Experiments conducted in this study highlight that the proposed technique helps to achieve a stable filter even though large bounds for the design variables are considered. The convergence rate, robustness, and computational speed of MRFO for all the considered problems are investigated. The influence of the control parameters of MRFO on the design performances is evaluated to gain insight into the interaction between the three foraging strategies of the algorithm. Extensive statistical performance analyses employing various non-parametric hypothesis tests concerning the design consistency and convergence are conducted for comparison of the proposed MRFO-based approach with six other metaheuristic search procedures to investigate the efficiency. The results on the mean square error metric also highlight the improved solution quality of the proposed approach compared to the various techniques published in the literature.

In this article, we offer simple random sampling (SRS) based efficient class of estimators of population mean Y ¯ utilizing additional information. The expression of the mean square error of the proposed class of estimators is deduced up to first degree approximation. The efficiency conditions are established which are enhanced numerically utilizing a simulation study consummated over symmetrical and asymmetrical populations. Real data sets are also utilized to exemplify the suggested estimators. The numerical findings are appeared rather acceptable demonstrating better advancement over the ordinary estimators. [ABSTRACT FROM AUTHOR]

This article presents a useful application of the Manta Ray Foraging Optimization (MRFO) algorithm for solving the adaptive infinite impulse response (IIR) system identification problem. The effectiveness of the proposed technique is validated on four benchmark IIR models for reduced order system identification. The stability of the proposed estimated IIR system is assured by incorporating a pole-finding and initialization routine in the search procedure of the MRFO algorithm and this algorithmic modification contributes to the MRFO algorithm when seeking stable IIR filter solutions. The absence of such a scheme, which is primarily the case with the majority of the recently published literature, may lead to the generation of an unstable IIR filter for unknown real-world instances (particularly when the estimation order increases). Experiments conducted in this study highlight that the proposed technique helps to achieve a stable filter even though large bounds for the design variables are considered. The convergence rate, robustness, and computational speed of MRFO for all the considered problems are investigated. The influence of the control parameters of MRFO on the design performances is evaluated to gain insight into the interaction between the three foraging strategies of the algorithm. Extensive statistical performance analyses employing various non-parametric hypothesis tests concerning the design consistency and convergence are conducted for comparison of the proposed MRFO-based approach with six other metaheuristic search procedures to investigate the efficiency. The results on the mean square error metric also highlight the improved solution quality of the proposed approach compared to the various techniques published in the literature. [ABSTRACT FROM AUTHOR]

In the sample survey theory, the crux of survey practitioners is to provide "accurate" estimators of the parameter of choice. The conventional theory depends on the regression/difference estimators as they correspond to the best linear unbiased (B L U) estimators. This paper suggests some optimal classes of estimators by modifying the conventional estimators under stratified ranked set sampling (S R S S). The characteristics of the suggested estimators are established to the first-order approximation. The performance of the suggested class of estimators under SRSS has been theoretically and experimentally shown to be superior to traditional estimators, particularly regression (B L U) estimators. [ABSTRACT FROM AUTHOR]

In this work, we presented a new model called the alpha power inverse power Burr-Hatke distribution (APIPBHD). It provides several greater advantages in fitting a variety of different types of data. Estimates of the model parameters are provided and based on traditional research methods. We established the superiority of the proposed distribution by utilizing the importance and adaptability of the APIPBHD compared to other well-known distributions. The real data set includes 63 observations, all of which were manufactured to approximate the strengths of glass fibers to highlight the relevance and flexibility of the provided technique. We proved our superiority using one set of real data. Finally, major findings and conclusions are recorded at the end of the paper. Also, we added future work on the upcoming research depending on the proposed model.

In this paper, two generalized class such as ratio to exponential and the difference type of estimators have been proposed for estimating the finite population mean by utilizing information on higher order moments of an auxiliary variable in the two traditional situations of non-response. The expressions of biases and mean squared errors of the estimators are derived up to first order of approximation. The efficiency comparisons of the proposed methods over other contemporary estimators are carried out on real data sets. A simulation study is also performed to support the present study. The performance of the proposed methods are better than the others existing methods. Results are interpreted and suitable recommendations are made.

The study's major goal is to design a superior creative distribution by using the Kumaraswamy-G family of distributions to power Frechet distribution. The Kumaraswamy power Frechet distribution (KPFD) with four parameters is the full name of the revolutionary model. The distribution's probability density function may take numerous forms and graphs and can be used to describe complicated data sets efficiently. Several features of the new distribution are obtained, including dependability, hazard rate, quantile, and moments. The estimation of the unknown parameters of KPFD are provided using the KPFD maximum likelihood estimation technique. Furthermore, a study was performed using the Monte Carlo simulation approach to test estimator accuracy regarding average bias (AB) and mean square error (MSE). Last but not least, two genuine data sets are supplied to compare the proposed model to existing models. [ABSTRACT FROM AUTHOR]

In this work, we presented a new model called the alpha power inverse power Burr-Hatke distribution (APIPBHD). It provides several greater advantages in fitting a variety of different types of data. Estimates of the model parameters are provided and based on traditional research methods. We established the superiority of the proposed distribution by utilizing the importance and adaptability of the APIPBHD compared to other well-known distributions. The real data set includes 63 observations, all of which were manufactured to approximate the strengths of glass fibers to highlight the relevance and flexibility of the provided technique. We proved our superiority using one set of real data. Finally, major findings and conclusions are recorded at the end of the paper. Also, we added future work on the upcoming research depending on the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, two generalized class such as ratio to exponential and the difference type of estimators have been proposed for estimating the finite population mean by utilizing information on higher order moments of an auxiliary variable in the two traditional situations of non-response. The expressions of biases and mean squared errors of the estimators are derived up to first order of approximation. The efficiency comparisons of the proposed methods over other contemporary estimators are carried out on real data sets. A simulation study is also performed to support the present study. The performance of the proposed methods are better than the others existing methods. Results are interpreted and suitable recommendations are made. [ABSTRACT FROM AUTHOR]

This study introduces some novel classes of estimators using ranked set sampling to evaluate the population mean utilizing additional information on an auxiliary variable. The suggested classes of estimators include mostly prominent estimators and are found to be more efficient regarding the other existing estimators available till date. The efficiency conditions are derived under which the discussed novel estimators dominate over the existing estimators. Further, to justify the propositions of this article, a computational study is executed utilizing some real and hypothetically produced populations and the results are found to be the rather satisfactory showing improvement over all the discussed works. [ABSTRACT FROM AUTHOR]