Measuring Tail Dependence for Aggregate Collateral Losses Using Bivariate Compound Shot-Noise Cox Process

In this paper, we introduce tail dependene measures for collateral losses from catastrophic events. To calculate these measures, we use bivariate compound process where a Cox process with shot noise intensity is used to count collateral losses. A homogeneous Poisson process is also examined as its counterpart for the case where the catastrophic loss frequency rate is deterministic. Joint Laplace transform of the distribution of the aggregate collateral losses is derived and joint Fast Fourier transform is used to obtain the joint distributions of aggregate collateral losses. For numerical illustrations, a member of Farlie-Gumbel-Morgenstern copula with exponential margins is used. The figures of the joint distributions of collateral losses, their contours and numerical calculations of risk measures are also provided.

Exploring Nutritional Compositions,Volatile Compounds,Health Benefits,Emerging Processing Technologies,and Potential Food Products of Glutinous Rice:A Review

Glutinous rice(Oryza sativa var.glutinosa)stands out as one of the most popular rice varieties globally,amidst thousands of rice cultivars.Its increasing popularity is attributed to its rich nutritional compositions and health benefits.This review aims to summarize the nutritional compositions,volatile compounds,and health benefits of glutinous rice.Further,in-depth studies are necessary to explore the utilization of glutinous rice in enhancing processing technologies and developing new food products.Glutinous rice has been shown to possess numerous health benefits,including antioxidant activity,bioactive compounds,anti-cancer properties,anti-inflammatory effects,anti-diabetic potential,and cholesterol-lowering effects.Besides its nutritional compositions,the major volatile compounds identified in glutinous rice could serve as a functional food for human consumption.Emerging processing technologies related to glutinous rice are elaborated to improve the latest developments for incorporating them into various food products.

Empirical Likelihood Statistical Inference for Compound Poisson Vector Processes under Infinite Covariance Matrix

The paper discusses the statistical inference problem of the compound Poisson vector process(CPVP)in the domain of attraction of normal law but with infinite covariance matrix.The empirical likelihood(EL)method to construct confidence regions for the mean vector has been proposed.It is a generalization from the finite second-order moments to the infinite second-order moments in the domain of attraction of normal law.The log-empirical likelihood ratio statistic for the average number of the CPVP converges to F distribution in distribution when the population is in the domain of attraction of normal law but has infinite covariance matrix.Some simulation results are proposed to illustrate the method of the paper.

Optimal Dividend Problem for a Compound Poisson Risk Model

In this note we study the optimal dividend problem for a company whose surplus process, in the absence of dividend payments, evolves as a generalized compound Poisson model in which the counting process is a generalized Poisson process. This model includes the classical risk model and the Pólya-Aeppli risk model as special cases. The objective is to find a dividend policy so as to maximize the expected discounted value of dividends which are paid to the shareholders until the company is ruined. We show that under some conditions the optimal dividend strategy is formed by a barrier strategy. Moreover, two conjectures are proposed.

One-Sample Bayesian Predictive Analyses for an Exponential Non-Homogeneous Poisson Process in Software Reliability

The Goel-Okumoto software reliability model, also known as the Exponential Nonhomogeneous Poisson Process,is one of the earliest software reliability models to be proposed. From literature, it is evident that most of the study that has been done on the Goel-Okumoto software reliability model is parameter estimation using the MLE method and model fit. It is widely known that predictive analysis is very useful for modifying, debugging and determining when to terminate software development testing process. However, there is a conspicuous absence of literature on both the classical and Bayesian predictive analyses on the model. This paper presents some results about predictive analyses for the Goel-Okumoto software reliability model. Driven by the requirement of highly reliable software used in computers embedded in automotive, mechanical and safety control systems, industrial and quality process control, real-time sensor networks, aircrafts, nuclear reactors among others, we address four issues in single-sample prediction associated closely with software development process. We have adopted Bayesian methods based on non-informative priors to develop explicit solutions to these problems. An example with real data in the form of time between software failures will be used to illustrate the developed methodologies.

Dividend Payments with a Hybrid Strategy in the Compound Poisson Risk Model

In this paper, a hybrid dividend strategy in the compound Poisson risk model is considered. In the absence of dividends, the surplus of an insurance company is modelled by a compound Poisson process. Dividends are paid at a constant rate whenever the modified surplus is in a interval;the premium income no longer goes into the surplus but is paid out as dividends whenever the modified surplus exceeds the upper bound of the interval, otherwise no dividends are paid. Integro-differential equations with boundary conditions satisfied by the expected total discounted dividends until ruin are derived;for example, closed-form solutions are given when claims are exponentially distributed. Accordingly, the moments and moment-generating functions of total discounted dividends until ruin are considered. Finally, the Gerber-Shiu function and Laplace transform of the ruin time are discussed.

Comparison of Ruin Probabilities in Compound Poisson Risk Model

Compound Poisson risk model has been simulated. It has started with exponential claim sizes. The simulations have checked for infinite ruin probabilities. An appropriate time window has been chosen to estimate and compare ruin probabilities. The infinite ruin probabilities of two-compound Poisson risk process have estimated and compared them with standard theoretical results.

Two-Sample Bayesian Predictive Analyses for an Exponential Non-Homogeneous Poisson Process in Software Reliability

The Goel-Okumoto software reliability model is one of the earliest attempts to use a non-homogeneous Poisson process to model failure times observed during software test interval. The model is known as exponential NHPP model as it describes exponential software failure curve. Parameter estimation, model fit and predictive analyses based on one sample have been conducted on the Goel-Okumoto software reliability model. However, predictive analyses based on two samples have not been conducted on the model. In two-sample prediction, the parameters and characteristics of the first sample are used to analyze and to make predictions for the second sample. This helps in saving time and resources during the software development process. This paper presents some results about predictive analyses for the Goel-Okumoto software reliability model based on two samples. We have addressed three issues in two-sample prediction associated closely with software development testing process. Bayesian methods based on non-informative priors have been adopted to develop solutions to these issues. The developed methodologies have been illustrated by two sets of software failure data simulated from the Goel-Okumoto software reliability model.

Impact of Preparation Process on the Protein Structure and on the Volatile Compounds in <i>Eisenia foetida</i>Protein Powders

Protein powders from Eisenia foetida were prepared using different drying processes and fractionation. Differential scanning calorimetry was used to show that heat denaturation occurred during the drying process above 42°C. Protein solubility was also studied. The addition of dissociating reagents allowed concluding that solubility was decreased during oven drying due to thermo denaturation including hydrogen bonds. The volatile compounds of the different powders were extracted by solid phase micro-extraction and identified by mass spectrometry. Volatile compounds were related to lipid oxidation and Maillard reactions occurring during the preparation of the powders. High drying temperatures led to more volatile compounds resulting from Maillard reactions. In the protein powder preparation process, a fractionation step led to a “pulp fraction” and a “juice fraction” of earthworms. The “pulp fraction” contained less odorant volatile compounds resulting from Maillard reactions than the “juice fraction” did.

带线性红利和干扰的复合Poisson-Geometric风险模型的破产问题

考虑了常利力环境下,包含线性红利、随机干扰和随机保费的复合P-G风险模型。通过应用全期望公式,推导出该模型的Gerber-Shiu函数及破产概率的更新方程。在不考虑分红且保费额和索赔额均服从指数分布时,进一步得到了破产概率所满足的具体微分方程,并求解得到了其解析表达式。通过数值实验,系统分析了多个关键因素对破产概率的具体影响,所得结论与保险公司的实际经营情况相吻合。

Source,Process and End Treatment Technology of Volatile Organic Compounds in Oil Storage

Oil storage is a source of volatile organic compounds( VOCs). Volatile organic compounds can cause different damages to the environment,animals and plants. Therefore, it is important to control the discharge of VOCs in oil storage. In this paper,the control technology of sources of VOCs pollution in oil storage was analyzed from the source,process and end treatment,and measures for the prevention and control of VOCs pollution in oil storage were proposed.

Effects of different thermal processing methods on bioactive components,phenolic compounds,and antioxidant activities of Qingke(highland hull-less barley)

Qingke(highland hull-less barley)is a grain replete with substantial nutrients and bioactive ingredients.In this study,we evaluated the effects of boiling(BO),steaming(ST),microwave baking(MB),far-infrared baking(FB),steam explosion(SE),and deep frying(DF)on bioactive components,phenolic compounds,and antioxidant activities of Qingke compared with the effects of traditional roast(TR).Results showed that the soluble dietary fiber,beta-glucan and water-extractable pentosans of Qingke in dry heat processes of TR,SE,MB and FB had a higher content compared with other thermal methods and had a better antioxidant activity of hydroxyl radical scavenging and a better reduction capacity,while those in wet heat processes of BO and ST had a better antioxidant activity of ABTS radical scavenging and a better Fe^(2+) chelating ability.DF-and SE-Qingke had a higher content of tocopherol,phenolic,and flavonoid.Overall,6 free phenolic compounds and 12 bound phenolic compounds of Qingke were identified,and free phenolic compounds suffered more damage during thermal processing.Principal component analysis showed that SE had more advantages in retaining and improving the main biological active ingredients of Qingke,and it may be the best method for treating Qingke.

Probability Distributions Arising in Connection with the Inspection Paradox for the Poisson Process

The Inspection Paradox refers to the fact that in a Renewal Process, the length of the interarrival period which contains a fixed time is stochastically larger than the length of a typical interarrival period. To provide a more complete understanding of this phenomenon, conditioning arguments are used to obtain the distributions and moments of the lengths of the interarrival periods other than the one containing this fixed time for the case of the time-homogeneous Poisson Process. Distributions of the waiting times for events that occur both before and after this fixed time are derived. This provides a fairly complete probabilistic analysis of the Inspection Paradox.

One-Sample Bayesian Predictive Analyses for a Nonhomogeneous Poisson Process with Delayed S-Shaped Intensity Function Using Non-Informative Priors

The delayed S-shaped software reliability growth model (SRGM) is one of the non-homogeneous Poisson process (NHPP) models which have been proposed for software reliability assessment. The model is distinctive because it has a mean value function that reflects the delay in failure reporting: there is a delay between failure detection and reporting time. The model captures error detection, isolation, and removal processes, thus is appropriate for software reliability analysis. Predictive analysis in software testing is useful in modifying, debugging, and determining when to terminate software development testing processes. However, Bayesian predictive analyses on the delayed S-shaped model have not been extensively explored. This paper uses the delayed S-shaped SRGM to address four issues in one-sample prediction associated with the software development testing process. Bayesian approach based on non-informative priors was used to derive explicit solutions for the four issues, and the developed methodologies were illustrated using real data.

A Bayesian Inference of Non-Life Insurance Based on Claim Counting Process with Periodic Claim Intensity

The aim of this study is to propose an estimation approach to non-life insurance claim counts related to the insurance claim counting process, including the non-homogeneous Poisson process (NHPP) with a bell-shaped intensity and a beta-shaped intensity. The estimating function, such as the zero mean martingale (ZMM), is used as a procedure for parameter estimation of the insurance claim counting process, and the parameters of model claim intensity are estimated by the Bayesian method. Then,Λ(t), the compensator of N(t) is proposed for the number of claims in a time interval (0,t]. Given the process over the time interval (0,t]., the situations are presented through a simulation study and some examples of these situations are also depicted by a sample path relating N(t) to its compensatorΛ(t).

带投资和分红策略下复合Poisson-Geometric风险模型的研究

考虑投资和分红策略下,建立带有混合收费及索赔计数服从复合Poisson-Geometric过程的风险模型。运用全期望公式与积分变换公式,研究该模型红利付款现值期望函数满足的微积分方程和特定指数分布下满足的微分方程及解析解,通过数值模拟分析了固定保费率、初始资本、投资资产、索赔强度和红利边界对红利付款现值期望函数的影响,并分析其经济意义。

The Conditional Poisson Process and the Erlang and Negative Binomial Distributions

It is a well known fact that for the hierarchical model of a Poisson random variable Y?whose mean has an Erlang distribution, the unconditional distribution of Y is negative binomial. However, the proofs in the literature [1] [2] provide no intuitive understanding as to why this result should be true. It is the purpose of this manuscript to give a new proof of this result which provides such an understanding. The memoryless property of the exponential distribution allows one to conclude that the events in two independent Poisson processes may be regarded as Bernoulli trials, and this fact is used to achieve the research purpose. Another goal of this manuscript is to give another proof of this last fact which does not rely on the memoryless property.

A Statistical Analysis of Intensities Estimation on the Modeling of Non-Life Insurance Claim Counting Process

This study presents an estimation approach to non-life insurance claim counts relating to a specified time. The objective of this study is to estimate the parameters in non-life insurance claim counting process, including the homogeneous Poisson process (HPP) and the non-homogeneous Poisson process (NHPP) with a bell-shaped intensity. We use the estimating function, the zero mean martingale (ZMM) as a procedure of parameter estimation in the insurance claim counting process. Then, Λ(t) , the compensator of is proposed for the number of claims in the time interval . We present situations through a simulation study of both processes on the time interval . Some examples of the situations in the simulation study are depicted by a sample path relating to its compensator Λ(t). In addition, an example of the claim counting process illustrates the result of the compensator estimate misspecification.

Large Deviation Principle for a Form of Compound Nonhomogeneous Poisson Process

By the Cramér method, the large deviation principle for a form of compound Poisson process S(t)=∑N(t)i=1h(t-Si)Xi is obtained,where N(t), t>0, is a nonhomogeneous Poisson process with intensity λ(t)>0, Xi, i≥1, are i.i.d. nonnegative random variables independent of N(t), and h(t), t>0, is a nonnegative monotone real function. Consequently, weak convergence for S(t) is also obtained.

带有投资收益率和双保费复合Poisson-Geometric风险模型的研究

研究了保费收入为线性增长和随机保费的风险模型,且随机保费的保单数服从复合Poisson过程,理赔次数服从复合Poisson-Geometric过程.应用全概率公式和积分变换公式,推导了该模型Gerber-Shiu折现罚金函数满足的更新方程,并当随机保费、理赔过程均服从特定指数分布时,得到了该模型破产概率的解析解,最后通过数值模拟对理论进行了分析验证.