Recursions for the Individual Risk Model

In the actuarial literature, several exact and approximative recursive methods have been proposed for calculating the distribution of a sum of mutually independent compound Bernoulli distributed random variables. In this paper, we give an overview of these methods. We compare their performance with the straight- forward convolution technique by counting the number of dot operations involved in each method. It turns out that in many practicle situations, the recursive methods outperform the convolution method.

Precise Large Deviations for a Customer-based Individual Risk Model

In this paper, we propose a customer-based individual risk model, in which potential claims by customers are described as i.i.d, heavy-tailed random variables, but different insurance policy holders are allowed to have different probabilities to make actual claims. Some precise large deviation results for the prospectiveoss process are derived under certain mild assumptions, with emphasis on the case of heavy-tailed distribution function class ERV （extended regular variation）. Lundberg type limiting results on the finite time ruin probabilities are also investigated.

Quantitative Structural Models to Assess Credit Risk on Individuals

Default Probabilities quantitatively measures the credit risk that a borrower will be unable or unwilling to repay its debt. An accurate model to estimate, as a function of time, these default probabilities is of crucial importance in the credit derivatives market. In this work, we adapt Merton’s [1] original works on credit risk, consumption and portfolio rules to model an individual wealth scenario, and apply it to compute this individual default probabilities. Using our model, we also compute the time depending individual default intensities, recovery rates, hazard rate and risk premiums. Hence, as a straight-forward application, our model can be used as novel way to measure the credit risk of individuals.

Blood pressure differences in people with various individual characteristics in Guangdong Province

Objective To study the differences in blood pressure (BP) levels and the main factors raising BP among the population in Guangdong Province. Methods The data analyzed stem from the sampling survey of hypertension in Guangdong Province in 1991, covering 42, 894 subjects over 15 years old. Individual characteristics included age, sex, occupation, education, smoking, alcohol drinking and body mass index (BMI) . Results Systolic and diastolic BP increased with age. The hypertension prevalence rate in male is higher than in female. The age - adjusted prevalence rate in office personnel is the highest (12.9 % ) among all occupations. It was increased with education level and BMI (in people educated at university and over is 13. 1 % ), and higher in smokers and alcohol-drinkers than non-smokers and non -alcohol-drinkers. Conclusions Age, occupation, education, smoking, alcohol drinking and BMI all effect BP. These risk factors should be reduced in the Guangdong population.

Existence and Uniqueness of Positive Solutions for a System of Multi-order Fractional Differential Equations

In this paper, we study a class of ruin problems, in which premiums and claims are dependent. Under the assumption that premium income is a stochastic process, we raise the model that premiums and claims are dependent, give its numerical characteristics and the ruin probability of the individual risk model in the surplus process. In addition, we promote the number of insurance policies to a Poisson process with parameter λ, using martingale methods to obtain the upper bound of the ultimate ruin probability.

Theory of mind impairments in individuals at ultra-high risk for psychosis

Objective To determine deficits in theory of mind in individuals at ultra-high risk for psychosis(UHR),and to explore the relations between theory of mind and symptom severity.Methods Twenty seven ultra-high-risk patients in clinics of the Peking University Sixth

AN EMPIRICAL FEASIBILITY STUDY OF SOCIETAL RISK CLASSIFICATION TOWARD BBS POSTS

Societal risk classification is the fundamental issue for online societal risk monitoring. To show the challenge and feasibility of societal risk classification toward BBS posts, an empirical analysis is implemented in this paper. Through effectiveness analysis, Support Vector Machine based on Bag-Of-Words （BOW-SVM） is adopted for challenge validation, and the distributed document embeddings of BBS posts generated by Paragraph Vector are applied to feasibility study. Based on BOW-SVM, cross-validations of BBS posts labeled by different groups and annotators are conducted. The big fluctuation of cross-validation results indicates the differences of individual risk perceptions, which brings more challenges to societal risk classification. Furthermore, based on the distributed document embeddings of BBS posts, the pairwise similarities of more than 300 thousands BBS posts from different societal risk categories are compared. The higher similarities of BBS posts in the same societal risk category reveal that BBS posts in the same societal risk category share more features than BBS posts in different categories, which manifests the feasibility of societal risk classification of BBS posts, and also reflects the possibility to improve the performance of societal risk monitoring.