Assessing the Performance of Some Ranked Set Sampling Designs Using Hybrid Approach

In this paper,a joint analysis consisting of goodness-of-fit tests and Markov chain Monte Carlo simulations are used to assess the performance of some ranked set sampling designs.The Markov chain Monte Carlo simulations are conducted when Bayesian methods with Jeffery’s priors of the unknown parameters of Weibull distribution are used,while the goodness of fit analysis is conducted when the likelihood estimators are used and the corresponding empirical distributions are obtained.The ranked set sampling designs considered in this research are the usual ranked set sampling,extreme ranked set sampling,median ranked set sampling,and neoteric ranked set sampling designs.An intensive Monte Carlo simulation study is conducted using Lindley’s approximation algorithm to compute the different designs’-based estimators.The study showed that the dependent design“neoteric ranked set sampling design”is superior to other ranked set designs and the total relative efficiency is higher than the other designs’total relative efficiency.

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’performance.The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance.The work of this paper consists of two parts.The first part is based on collecting data of employees to calculate and illustrate the performance of each employee.The second part is based on the classification and prediction techniques of the employee performance.This model is designed to help companies in their decisions about the employees’performance.The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features.Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years.Results also show that the Grasshopper Optimization,followed by“KF”with the Gradient Boosting Tree as classifier and predictor,is characterized by a high accuracy.The proposed algorithm is compared with other known techniques where our results are fund to be superior.

Topp-Leone Odd Fréchet Generated Family of Distributions with Applications to COVID-19 Data Sets

Recent studies have pointed out the potential of the odd Fréchet family(or class)of continuous distributions in fitting data of all kinds.In this article,we propose an extension of this family through the so-called“Topp-Leone strategy”,aiming to improve its overall flexibility by adding a shape parameter.The main objective is to offer original distributions with modifiable properties,from which adaptive and pliant statistical models can be derived.For the new family,these aspects are illustrated by the means of comprehensive mathematical and numerical results.In particular,we emphasize a special distribution with three parameters based on the exponential distribution.The related model is shown to be skillful to the fitting of various lifetime data,more or less heterogeneous.Among all the possible applications,we consider two data sets of current interest,linked to the COVID-19 pandemic.They concern daily cases confirmed and recovered in Pakistan from March 24 to April 28,2020.As a result of our analyzes,the proposed model has the best fitting results in comparison to serious challengers,including the former odd Fréchet model.

Kumaraswamy Inverted Topp–Leone Distribution with Applications to COVID-19 Data

In this paper, an attempt is made to discover the distributionof COVID-19 spread in different countries such as;Saudi Arabia, Italy,Argentina and Angola by specifying an optimal statistical distribution for analyzing the mortality rate of COVID-19. A new generalization of the recentlyinverted Topp Leone distribution, called Kumaraswamy inverted Topp–Leonedistribution, is proposed by combining the Kumaraswamy-G family and theinverted Topp–Leone distribution. We initially provide a linear representationof its density function. We give some of its structure properties, such as quantile function, median, moments, incomplete moments, Lorenz and Bonferronicurves, entropies measures and stress-strength reliability. Then, Bayesian andmaximum likelihood estimators for parameters of the Kumaraswamy invertedTopp–Leone distribution under Type-II censored sample are considered.Bayesian estimator is regarded using symmetric and asymmetric loss functions. As analytical solution is too hard, behaviours of estimates have beendone viz Monte Carlo simulation study and some reasonable comparisonshave been presented. The outcomes of the simulation study confirmed theefficiencies of obtained estimates as well as yielded the superiority of Bayesianestimate under adequate priors compared to the maximum likelihood estimate.Application to COVID-19 in some countries showed that the new distributionis more appropriate than some other competitive models.

On the Role of Consumer Preferences in the Coordination among Health Insurers under Regulated Competition

This paper addresses the coordination problem of price and quality setting insurers, arising on the health insurance market under regulated competition, as introduced, for instance, in the Netherlands in 2006. We use an experimental study to gain novel insights on the impact of the uncertainty about consumer preferences on the coordination problem. This fundamental uncertainty implies uncertainty about the identity of the payoff dominant equilibrium, while the risk dominant equilibrium is independent of the state of the world. The experimental results show that insurers are more likely to coordinate on the payoff dominant equilibrium under incomplete information. When insurers face not only strategic but also fundamental uncertainty in the coordination problem, they delay the response to the risk dominant strategy, and persist longer in trying to coordinate on the payoff dominant equilibrium. For the market we study, this implies that the co-ordination under incomplete information will result in consumers paying higher prices, in contrast to the original objectives of the regulated competition.

机电一体化培训和教育中的电子移动学习课件开发(英文)

Knowledge is essential for the competitiveness of individuals as well as organizations.Thus the application of the latest methodologies and technologies are utilized to support knowledge acquisition,warehousing,distribution,and transfer.Means and methods of web 2.0 are useful to support this procedure.Especially,highly complex and very dynamic knowledge domains have to be accessible and applicable in the framework of learning network communities,including the stakeholders of training and education.Mechatronics for example is such an interdisciplinary,dynamic field of research and application.Based on intelligence,software,and hardware it is requiring special approaches for developing a courseware based learning and knowledge transfer environment.After defining the specifics of mechatronics education and postgraduate training in the context of e-education,the concepts of the development and utilization of mechatronic courseware can be deduced from e-learning 2.0 and mobile learning facilities,possibilities,and abilities.Mechatronic courseware will be developed by using authoring software and embedding the material into learning management systems with respect to general methods and rules of modern system and software development.As an example,the courseware is used for vocational training and further education especially in cooperation networks of educational institutions and SME.

Development of an early warning information infrastructure using spatial web services technology

Current high-performance monitoring and early warning systems are compara-tively expensive and inflexible.The project described here aims for a systematic development of a prototypical alarm and early warning system for landslides utilising low-cost ad hoc wireless sensor networks and flexible spatial web service technology.Using web-based services as tools to exchange environmental data is an important step towards addressing today’s larger scale environmental problems.The project’s information system functions for notification,data access and analysis are implemented utilising Open Geospatial Consortium web service specifications.Approaches to implementing the orchestration of operation workflows and production of near real-time information products are illustrated.