Abstract:The generalized log-logistic distribution is especially useful for modelling survival data with variable hazard rate shapes because it extends the log-logistic distribution by adding an extra parameter to the classical distribution, resulting in greater flexibility in analyzing and modelling various data types. We derive the fundamental mathematical and statistical properties of the proposed distribution in this paper. Many well-known lifetime special submodels are included in the proposed distribution, includ… Show more
“…Muse et al [ 24 ] applied Bayesian and classical approaches for inference about a generalized LL distribution. Alfaer et al [ 25 ] introduced exponentiated Marshal-Olkin extension of the LL model for modeling high tail data in insurance claims.…”
In information science, modern and advanced computational methods and tools are often used to build predictive models for time-to-event data analysis. Such predictive models based on previously collected data from patients can support decision-making and prediction of clinical data. Therefore, a new simple and flexible modified log-logistic model is presented in this paper. Then, some basic statistical and reliability properties are discussed. Also, a graphical method for determining the data from the log-logistic or the proposed modified model is presented. Some methods are applied to estimate the parameters of the presented model. A simulation study is conducted to investigate the consistency and behavior of the discussed estimators. Finally, the model is fitted to two data sets and compared with some other candidates.
“…Muse et al [ 24 ] applied Bayesian and classical approaches for inference about a generalized LL distribution. Alfaer et al [ 25 ] introduced exponentiated Marshal-Olkin extension of the LL model for modeling high tail data in insurance claims.…”
In information science, modern and advanced computational methods and tools are often used to build predictive models for time-to-event data analysis. Such predictive models based on previously collected data from patients can support decision-making and prediction of clinical data. Therefore, a new simple and flexible modified log-logistic model is presented in this paper. Then, some basic statistical and reliability properties are discussed. Also, a graphical method for determining the data from the log-logistic or the proposed modified model is presented. Some methods are applied to estimate the parameters of the presented model. A simulation study is conducted to investigate the consistency and behavior of the discussed estimators. Finally, the model is fitted to two data sets and compared with some other candidates.
“…erefore, how to reduce the execution time under the premise of ensuring the effect is the focus of future work. (2) is paper only studies the combination of several classic subjective weighting methods and objective weighting methods, and there are more combinations of combination weighting methods that can be used, such as game theory.…”
Section: Discussionmentioning
confidence: 99%
“…In 2013, the ER rule, which considers the weight and reliability of evidence, was established by Yang and Xu [ 1 ]. The ER rule, which is an extension of the D-S theory [ 2 , 3 ], clearly distinguishes the importance and reliability of evidence and constitutes a general joint probabilistic reasoning process. The counterintuitive problem encountered in Dempster's rule is solved by assigning weight and reliability to evidence.…”
Section: Introductionmentioning
confidence: 99%
“…e ER rule, which is an extension of the D-S theory [2,3], clearly distinguishes the importance and reliability of evidence and constitutes a general joint probabilistic reasoning process.…”
As an extension of Dempster–Shafer (D-S) theory, the evidential reasoning (ER) rule can be used as a combination strategy in ensemble learning to deeply mine classifier information through decision-making reasoning. The weight of evidence is an important parameter in the ER rule, which has a significant effect on the result of ensemble learning. However, current research results on the weight of evidence are not ideal, leveraging expert knowledge to assign weights leads to the excessive subjectivity, and using sample statistical methods to assign weights relies too heavily on the samples, so the determined weights sometimes differ greatly from the actual importance of the attributes. Therefore, to solve the problem of excessive subjectivity and objectivity of the weights of evidence, and further improve the accuracy of ensemble learning based on the ER rule, we propose a novel combination weighting method to determine the weight of evidence. The combined weights are calculated by leveraging our proposed method to combine subjective and objective weights of evidence. The regularization of these weights is studied. Then, the evidential reasoning rule is used to integrate different classifiers. Five case studies of image classification datasets have been conducted to demonstrate the effectiveness of the combination weighting method.
“…The distribution family can be parametric, semiparametric, or nonparametric. Parametric models produce more efficient estimates with lower standard errors than nonparametric and semiparametric models [ 1 ], more specifically, if the distributional assumption is correct. In general, probability distributions have been widely used to model lifetime data in a variety of fields, particularly biomedical sciences and engineering.…”
The goal of this paper is to develop an optimal statistical model to analyze COVID-19 data in order to model and analyze the COVID-19 mortality rates in Somalia. Combining the log-logistic distribution and the tangent function yields the flexible extension log-logistic tangent (LLT) distribution, a new two-parameter distribution. This new distribution has a number of excellent statistical and mathematical properties, including a simple failure rate function, reliability function, and cumulative distribution function. Maximum likelihood estimation (MLE) is used to estimate the unknown parameters of the proposed distribution. A numerical and visual result of the Monte Carlo simulation is obtained to evaluate the use of the MLE method. In addition, the LLT model is compared to the well-known two-parameter, three-parameter, and four-parameter competitors. Gompertz, log-logistic, kappa, exponentiated log-logistic, Marshall–Olkin log-logistic, Kumaraswamy log-logistic, and beta log-logistic are among the competing models. Different goodness-of-fit measures are used to determine whether the LLT distribution is more useful than the competing models in COVID-19 data of mortality rate analysis.
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