Uncertain hypothesis test for uncertain differential equations

Uncertain hypothesis test is a statistical tool that uses uncertainty theory to determine whether some hypotheses are correct or not based on observed data. As an application of uncertain hypothesis test, this paper proposes a method to test whether an uncertain differential equation fits the observed data or not. In order to demonstrate the test method, some numerical examples are provided. Finally, both uncertain currency model and stochastic currency model are used to model US Dollar to Chinese Yuan (USD–CNY) exchange rates. As a result, it is shown that the uncertain currency model fits the exchange rates well, but the stochastic currency model does not.

Uncertain Logistic Growth Model for Confirmed COVID-19 Cases in Brazil

The pandemic COVID-19 gives rise to a serious threat to people's health, economic development and social stability. This paper employs uncertain regression analysis to model the cumulative number of COVID-19 infection in Brazil. Some fundamental knowledge about the uncertain regression analysis is reviewed firstly. Then parameter estimation, residual analysis, uncertain hypothesis test and the forecast value and confidence interval are studied for confirmed COVID-19 cases in Brazil. As a byproduct, the reason for using uncertain regression analysis instead of probabilistic regression analysis is explained by analyzing the characteristics of the residual plot. All the analysis and prediction are devoted to proposing some theoretical supports for the epidemic prevention and control to some extent. © 2022 World Scientific Publishing Company.

Analysis of Death Toll from COVID-19 in China with Uncertain Time Series and Uncertain Regression Analysis

This paper employs uncertain time series and uncertain regression analysis to model the evolution of the cumulative death toll from COVID-19 in China. Then parameter estimation, residual analysis, forecast and confidence interval are investigated. Uncertain hypothesis test is proposed to determine whether the estimated uncertain statistical models are appropriate. By analyzing the characteristics of the residual plot, the reason of using uncertain statistics instead of probabilistic statistics is explained. © 2022 World Scientific Publishing Company.

Multi-period portfolio selection based on uncertainty theory with bankruptcy control and liquidity

Due to the impact of uncertain events, such as the 2008 financial crisis and the outburst of COVID-19 pandemic, the experts’ evaluations information is becoming increasingly important. This paper considers a multi-period portfolio optimization problem under uncertain circumstance, and the return rates of risky securities are regarded as uncertain variables, where the uncertainty theory is used to deal with experts’ evaluations. In light of the complexity of financial markets, we formulate an uncertain multi-period mean-entropy-variance model, where bankruptcy, liquidity, diversification and self-financing are considered as realistic constraints. Furthermore, the maximum return and the minimum risk are both achieved in a single-objective model through the normalization method. Then the equivalent deterministic forms of two secondary models for main model are provided. In addition, we develop a modified root system growth algorithm, which is more suitable for the proposed model. Finally, the effectiveness of the proposed model and designed algorithm is confirmed by numerical simulations.

The Challenges of Chinese University Leaders During the COVID-19 Pandemic Period: A Case Study Approach.

The outbreak of COVID-19 had a profound impact on the practice of university leadership in China. This study employs a case study as the research method, interviewing five Heads of the Departments from the Z University in China to examine the challenges to leadership in Chinese universities during the COVID-19 pandemic and explores effective countermeasures. Research findings reveal that the challenges they faced manifested in the government's closed management requirements and the students' demands for freedom of entry and exit, the dynamic and flexible disciplinary development and the rigid teaching evaluation, and big data-enabled governance and the habit of human experience-oriented management. In response to these challenges, this study proposes suggestions for the Z University leaders in the post-pandemic era: establishing rules and regulations with a relaxed degree, tolerating ambiguity in online teaching, improving the ability of intelligent technology, and taking opportunities to learn.

ConstructionofBeliefSystemsandControversies aroundCovid-19

The Covid-19 pandemic saw the emergence of public opinion in the public health decision that often was strongly contested. Relying on both recent work in behavioral psychology and psycho-sociology, as well as on the theory of ambiguity, this contribution attempts to analyze the reasons for this doubt. Two types of patients will be distinguished, “analytical” and “intuitive”, to explain the choices for a given action. This a priori distinction is not sufficient to explain the choices of a given type because the degree of aversion to ambiguity and the attitude towards ambiguity can modify the latter. It is in fact the belief in the capacity of one treatment to heal in relation to another that proves to be fundamental in determining the choice for one of them. © Dalloz.

A mixed 0-1 programming approach for multiple attribute strategic weight manipulation based on uncertainty theory

In practical multiple attribute decision making (MADM) problems, the interest groups or individuals intentionally set attribute weights to achieve their own benefits. In this case, the rankings of different alternatives are changed strategically, which is called the strategic weight manipulation in MADM. Sometimes, the attribute values are given with imprecise forms. Several theories and methods have been developed to deal with uncertainty, such as probability theory, interval values, intuitionistic fuzzy sets, hesitant fuzzy sets, etc. In this paper, we study the strategic weight manipulation based on the belief degree of uncertainty theory, with uncertain attribute values obeying linear uncertain distributions. It allows the attribute values to be considered as a whole in the operation process. A series of mixed 0-1 programming models are constructed to set a strategic weight vector for a desired ranking of a particular alternative. Finally, an example based on the assessment of the performance of COVID-19 vaccines illustrates the validity of the proposed models. Comparison analysis shows that, compared to the deterministic case, it is easier to manipulate attribute weights when the attribute values obey the linear uncertain distribution. And a further comparative analysis highlights the performance of different aggregation operators in defending against the strategic manipulation, and highlights the impacts on ranking range under different belief degrees.

Uncertain regression model with autoregressive time series errors.

Uncertain regression model is a powerful analytical tool for exploring the relationship between explanatory variables and response variables. It is assumed that the errors of regression equations are independent. However, in many cases, the error terms are highly positively autocorrelated. Assuming that the errors have an autoregressive structure, this paper first proposes an uncertain regression model with autoregressive time series errors. Then, the principle of least squares is used to estimate the unknown parameters in the model. Besides, this new methodology is used to analyze and predict the cumulative number of confirmed COVID-19 cases in China. Finally, this paper gives a comparative analysis of uncertain regression model, difference plus uncertain autoregressive model, and uncertain regression model with autoregressive time series errors. From the comparison, it is concluded that the uncertain regression model with autoregressive time series errors can improve the accuracy of predictions compared with the uncertain regression model.