Safety Analysis of Riding at Intersection Entrance Using Video Recognition Technology

To study riding safety at intersection entrance,video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian method.It is analyzed the relationship among the width of nonmotorized lanes at the entrance lane of the intersection,the vehicle-bicycle soft isolation form of the entrance lane of intersection,the traffic volume of right-turning motor vehicles and straight-going non-motor vehicles,the speed of right-turning motor vehicles,and straight-going non-motor vehicles,and the conflict between right-turning motor vehicles and straight-going nonmotor vehicles.Due to the traditional statistical methods,to overcome the discreteness of vehicle-bicycle conflict data and the differences of influencing factors,the Bayesian random effect Poisson-log-normal model and random effect negative binomial regression model are established.The results show that the random effect Poisson-log-normal model is better than the negative binomial distribution of random effects;The width of non-motorized lanes,the form of vehicle-bicycle soft isolation,the traffic volume of right-turning motor vehicles,and the coefficients of straight traffic volume obey a normal distribution.Among them,the type of vehicle-bicycle soft isolation facilities and the vehicle-bicycle traffic volumes are significantly positively correlated with the number of vehicle-bicycle conflicts.The width of non-motorized lanes is significantly negatively correlated with the number of vehicle-bicycle conflicts.Peak periods and flat periods,the average speed of right-turning motor vehicles,and the average speed of straight-going non-motor vehicles have no significant influence on the number of vehicle-bicycle conflicts.

Predictors of the Aggregate of COVID-19 Cases and Its Case-Fatality: A Global Investigation Involving 120 Countries

Objective: Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemic suggested that the coronavirus was associated with more serious chronic diseases and mortality particularly in patients with chronic diseases regardless of country and age. Therefore, there is a need to understand how common comorbidities and other factors are associated with the risk of death due to COVID-19 infection. Our investigation aims at exploring this relationship. Specifically, our analysis aimed to explore the relationship between the total number of COVID-19 cases and mortality associated with COVID-19 infection accounting for other risk factors. Methods: Due to the presence of over dispersion, the Negative Binomial Regression is used to model the aggregate number of COVID-19 cases. Case-fatality associated with this infection is modeled as an outcome variable using machine learning predictive multivariable regression. The data we used are the COVID-19 cases and associated deaths from the start of the pandemic up to December 02-2020, the day Pfizer was granted approval for their new COVID-19 vaccine. Results: Our analysis found significant regional variation in case fatality. Moreover, the aggregate number of cases had several risk factors including chronic kidney disease, population density and the percentage of gross domestic product spent on healthcare. The Conclusions: There are important regional variations in COVID-19 case fatality. We identified three factors to be significantly correlated with case fatality.

Performance Evaluation of Motor Insurance Companies: Panel Data Evidence from Nigeria

This paper evaluated performance of motor insurance companies in Nigeria. The objectives were to determine the following: 1) significant effects of claims settlements on motor insurance firms’ earned premium;2) differences in managerial/technological capabilities among the companies and 3) effects of policy (or time effect) on insurance firms’ output within the study period. Panel data obtained for this study comprised operational data on premium earned and direct claims settlement by these companies over a period of six (6) years. Using panel data statistical models, we found that direct claims settlement negatively affected insurance companies’ earned premium. Also, significant differences in technological and managerial capabilities were found to exist among the companies, though only one company exhibited this heterogeneity. Besides, there were no policy impacts (or time effect) on vehicle insurance firms’ output in the study period. Policy implications of the results were discussed.

Hierarchical linear regression models for conditional quantiles

The quantile regression has several useful features and therefore is gradually developing into a comprehensive approach to the statistical analysis of linear and nonlinear response models,but it cannot deal effectively with the data with a hierarchical structure.In practice,the existence of such data hierarchies is neither accidental nor ignorable,it is a common phenomenon.To ignore this hierarchical data structure risks overlooking the importance of group effects,and may also render many of the traditional statistical analysis techniques used for studying data relationships invalid.On the other hand,the hierarchical models take a hierarchical data structure into account and have also many applications in statistics,ranging from overdispersion to constructing min-max estimators.However,the hierarchical models are virtually the mean regression,therefore,they cannot be used to characterize the entire conditional distribution of a dependent variable given high-dimensional covariates.Furthermore,the estimated coefficient vector (marginal effects)is sensitive to an outlier observation on the dependent variable.In this article,a new approach,which is based on the Gauss-Seidel iteration and taking a full advantage of the quantile regression and hierarchical models,is developed.On the theoretical front,we also consider the asymptotic properties of the new method,obtaining the simple conditions for an n1/2-convergence and an asymptotic normality.We also illustrate the use of the technique with the real educational data which is hierarchical and how the results can be explained.

邻里效应对中国自然保护区家庭参与生态系统服务付费项目的影响--方法研究

Payments for Ecosystem Services(PES)programs have been implemented in both developing and developed countries to conserve ecosystems and the vital services they provide.These programs also often seek to maintain or improve the economic wellbeing of the populations living in the corresponding(usually rural)areas.Previous studies suggest that PES policy design,presence or absence of concurrent PES programs,and a variety of socioeconomic and demographic factors can influence decisions of households to participate or not in the PES program.However,neighborhood impacts on household participation in PES have rarely been addressed.This study explores potential neighborhood effects on villagers'enrollment in the Grain-to-Green Program(GTGP),one of the largest PES programs in the world,using data from China's Fanjingshan National Nature Reserve.We utilize a fixed effects logistic regression model in combination with the eigenvector spatial filtering(ESF)method to explore whether neighborhood size affects household enrollment in GTGP.By comparing the results with and without ESF,we find that the ESF method can help account for spatial autocorrelation properly and reveal neighborhood impacts that are otherwise hidden,including the effects of area of forest enrolled in a concurrent PES program,gender and household size.The method can thus uncover mechanisms previously undetected due to not taking into account neighborhood impacts and thus provides an additional way to account for neighborhood impacts in PES programs and other studies.