Modeling conflict risk with real-time traffic data for road safety assessment:a copula-based joint approach

This study proposes a conflict-based traffic safety assessment method by associating conflict frequency and severity with shorttermtraffic characteristics. Instead of analysing historical crash data, this study employs microscopic trajectory data to quantify therelationship between conflict risk and traffic characteristics. The time-to-collision (TTC) index is used to detect conflicts, and a severityindex (SI) is proposed on the basis of time-integrated TTC. With SI, the k-means algorithm is applied to classify the conflict severitylevel. Then the severity of regional conflict risk is split to three levels. Zero truncated Poisson regression and ordered logit regressionmethods are employed to estimate the effects of short-term traffic characteristics on conflict frequency and severity, respectively.Furthermore, the copula-based joint modelling method is applied to explore the potential non-linear dependency of conflict riskoutcomes. A total of 18 copula models are tested to select the optimal ones. The HighD dataset from Germany is utilized to examinethe proposed framework. Both between-lane and within-lane factors are considered. Results show that the correlations betweentraffic characteristics and conflict risk are significant, and the dependency of conflict outcomes varies among different severity levels.The difference of speed variation between lanes significantly influences the conflict frequency and severity simultaneously. Findingsindicate that the proposed method is practicable to assess real-time traffic safety within a specific region by using short-term (30-second time interval) traffic characteristics. This study also contributes to develop targeted proactive safety strategies by evaluatingroad safety based on conflict risk, and considering different severity levels.

Strategic Asset Allocation for China's Foreign Reserves:A Copula Approach

In this paper, we study strategic asset allocation for China＇s foreign reserves using a risk- based approach. Four aspects of the risk management are investigated： an investment universe, dependence structure, allocation strategies under risk minimization and trade-off between risks and returns. A regime-switching copula model is developed to investigate the dynamic dependence between assets. One regime emphasizes a short-term safe asset and the other regime emphasizes a long-term safe asset. The optimal allocation is derived following two strategies： risk minimization and trade-off between risks and returns in utility maximization with disappointment avoidance, lf the central bank focuses solely on risk minimization, the asymmetries in the asset return dependence encourage the flight to safety. However, if higher risks are allowed in exchange for higher returns, even the exchange is very conservative, and the asymmetries would discourage the flight to safety. Therefore, we suggest that China should mitigate its flight to safety after 2008 and increase holdings of short-term bank deposits, long-term treasury bonds and euro bonds.

A Markov Copula Model with Regime Switching and Its Application

Regime switching,which is described by a Markov chain,is introduced in a Markov copula model.We prove that the marginals（X,H^i）,i = 1,2,3 of the Markov copula model（X,H） are still Markov processes and have martingale property.In this proposed model,a pricing formula of credit default swap（CDS） with bilateral counterparty risk is derived.

Driving role of climatic and socioenvironmental factors on human brucellosis in China:machine-learning-based predictive analyses

Background Brucellosis is a common zoonotic infectious disease in China.This study aimed to investigate the incidence trends of brucellosis in China,construct an optimal prediction model,and analyze the driving role of climatic factors for human brucellosis.Methods Using brucellosis incidence,and the socioeconomic and climatic data for 2014–2020 in China,we performed spatiotemporal analyses and calculated correlations with brucellosis incidence in China,developed and compared a series of regression and Seasonal Autoregressive Integrated Moving Average X(SARIMAX)models for brucellosis prediction based on socioeconomic and climatic data,and analyzed the relationship between extreme weather conditions and brucellosis incidence using copula models.Results In total,327,456 brucellosis cases were reported in China in 2014–2020(monthly average of 3898 cases).The incidence of brucellosis was distinctly seasonal,with a high incidence in spring and summer and an average annual peak in May.The incidence rate was highest in the northern regions’arid and continental climatic zones(1.88 and 0.47 per million people,respectively)and lowest in the tropics(0.003 per million people).The incidence of brucellosis showed opposite trends of decrease and increase in northern and southern China,respectively,with an overall severe epidemic in northern China.Most regression models using socioeconomic and climatic data cannot predict brucellosis incidence.The SARIMAX model was suitable for brucellosis prediction.There were significant negative correlations between the proportion of extreme weather values for both high sunshine and high humidity and the incidence of brucellosis as follows:high sunshine,r=−0.59 and−0.69 in arid and temperate zones;high humidity,r=−0.62,−0.64,and−0.65 in arid,temperate,and tropical zones.Conclusions Significant seasonal and climatic zone differences were observed for brucellosis incidence in China.Sunlight,humidity,and wind speed significantly influenced brucellosis.The SARIMAX model performed better for brucellosis prediction than did the regression model.Notably,high sunshine and humidity values in extreme weather conditions negatively affect brucellosis.Brucellosis should be managed according to the“One Health”concept.

Visual Perception-Based Fault Diagnosis in Froth Flotation Using Statistical Approaches

Froth flotation is an important mineral concentration technique.Faulty conditions in flotation processes may cause the huge waste of mineral resources and reagents,and consequently,may lead to deterioration in terms of benefits of flotation plants.In this paper,we propose a computer vision-aided fault detection and diagnosis approach for froth flotation.Specifically,a joint Gabor texture feature based on the Copula model is designed to describe froth images;a rejection sampling technique is developed to generate training sets from the quality distribution of real flotation products,and then an isolation forest-based fault detector is learned;and a fault diagnosis model based on spline regression is developed for root cause identification.Simulation experiments conducted on the historical industry data show that the proposed strategy has better performance than the alternative methods.Thereafter,the entire framework has been tested on a lead-zinc flotation plant in China.Experimental results have demonstrated the effectiveness of the proposed method.

Nonparametric Estimation of Interval-censored Failure Time Data in the Presence of Informative Censoring

Nonparametric estimation of a survival function is one of the most commonly asked questions in the analysis of failure time data and for this, a number of procedures have been developed under various types of censoring structures （Kalbfleisch and Prentice, 2002）. In particular, several algorithms are available for interval-censored failure time data with independent censoring mechanism （Sun, 2006; Turnbull, 1976）. In this paper, we consider the interval-censored data where the censoring mechanism may be related to the failure time of interest, for which there does not seem to exist a nonparametric estimation procedure. It is well-known that with informative censoring, the estimation is possible only under some assumptions. To attack the problem, we take a copula model approach to model the relationship between the failure time of interest and censoring variables and present a simple nonparametric estimation procedure. The method allows one to conduct a sensitivity analysis among others.

Regression Analysis of Interval-Censored Data with Informative Observation Times Under the Accelerated Failure Time Model

This paper discusses regression analysis of interval-censored failure time data arising from the accelerated failure time model in the presence of informative censoring.For the problem,a sieve maximum likelihood estimation approach is proposed and in the method,the copula model is employed to describe the relationship between the failure time of interest and the censoring or observation process.Also I-spline functions are used to approximate the unknown functions in the model,and a simulation study is carried out to assess the finite sample performance of the proposed approach and suggests that it works well in practical situations.In addition,an illustrative example is provided.

Pandemic Bonds Issued by the Chinese Government Supported Post-Disaster Recovery from COVID-19 Pandemic

During the SARS-CoV-2(COIVD-19)outbreak,China repeatedly stressed that the response to the pandemic required action at all levels of government,including the issuance of Pandemic Bonds to help the country return to work and production.However,studies on the effectiveness of Pandemic Bonds during that period are rare.Starting with China’s national financial bond market data after COVID-19 in 2020,this paper focuses on the correlation between the Credit Spreads of the relevant bonds and the corresponding bond market rate of return,based on the Copula model.The empirical analysis is also carried out for multiple dimensional groupings such as enterprises,industries,provinces,and bond maturities.The results show that there is a significant positive correlation between the Credit Spreads of Pandemic Bonds and market returns.In addition,the market correlation is higher for Pandemic Bonds issued in Hubei Province,which is at the center of the 2020 pandemic,and the shorter the maturity of the Pandemic Bond issued,the stronger the relationship with market returns.Finally,this paper provides recommendations for financial regulators and policy makers to consider in their decisions on how to build a more resilient financial system under heavy economic,fiscal,and social pressures.