基于一种新的风险度量的保费计算方法

本文经过研究发现ρ(X)=(E(X~2))~(1/2)作为纯粹风险测度有三大优点,进而在此基础上提出了一种新的保费计算方法,最后论证了该保费计算方法的合理性。     

基于分位回归的风险保费预测

风险保费预测是非寿险费率厘定的重要组成部分。在传统的分位回归厘定风险保费中,通常假设分位数水平是事先给定的,缺乏一定的客观性。为此,提出了一种应用分位回归厘定风险保费的新方法。基于破产概率确定保单组合的总风险保费,建立个体保单的分位回归模型,并与总风险保费建立等式关系,通过数值方法求解出分位数水平,实现对个体保单风险保费的预测。通过一组实际数据分析表明,该方法具有良好的预测效果。     

广义加权平衡指数损失函数下的信度保费

在经典的信度保费模型中,得到的信度保费估计均是考虑的是纯保费,然而在保险买务中,保险公司收取的保费不可能是纯保费,必须具有正的安全负荷.文章在指数保费原理的基础上,引入广义加权平衡指数损失函数计算了下一期的信度保费计算公式,从而得出Bayes保费可以写成信度保费形式.     

基于两阶段Expectile回归的风险保费定价

如何厘定风险保费是保险精算的核心研究内容之一。风险保费由纯保费和风险附加构成,通常采用广义线性模型厘定纯保费,应用各种保费原理计算风险附加。常用的保费原理包括期望值原理、标准差原理、分位数原理等。文章基于对Expectile理论性质的研究结果,提出Expectile保费原理,即通过两阶段Expectile回归预测每份保单的风险保费。类似于分位回归是对中位数回归的自然推广,Expectile回归是对均值回归的自然推广。应用分位回归相当于是在中位数的基础上计算风险附加,而应用Expectile回归可以看作是在均值（亦即纯保费）基础上计算风险附加,所以风险保费的计算结果更加合乎定价逻辑。基于一组公开数据的实证研究结果表明,两阶段Expectile回归在基尼系数指标下厘定风险保费要优于其他现有方法,对于提高保险定价的科学性和合理性具有重要的实际应用价值。     

财产总体损失保费计价的方法性研究

本文旨在研究个体损失分布与总体损失分布之间的关系,以及在个体损失分布类型确定的情况下如何近似计算总体损失分布的净保费和附加保费。     

Esscher保费的非参数估计

Esscher保费原理是非寿险精算中最重要的保费原理之一,在精算学中有重要的运用.文章研究了Esscher保费的非参数估计,并证明了估计的强相合性和渐近正态性,最后通过数值模拟的方法验证了估计的收敛速度及渐近正态性.     

交强险保费的公平性与保险公司的市场竞争

基于2009-2011年保险公司在各个地区经营交强险业务的实际数据,本文分析了当前的交强险保费水平在不同业务类型和不同地区之间存在的不公平性问题。通过赔付率和费用率的相关性分析,揭示了交强险费用率的异常变化和可能受到的人为干预。定义和计算了交强险的业务结构因子、地区分布因子、固定业务结构的风险选择因子和固定地区分布的风险选择因子,对保险公司在交强险市场上的竞争能力和风险选择能力进行了比较分析。最后通过建立赔付率和费用率的广义线性模型,发现保险公司降低交强险赔付率最为有效的途径是提高它在各个地区的风险选择能力,其次是增加在低风险地区的经营网点。     

浙江省保费收入与经济增长的相关性研究

近几年浙江保费收入保持高速增长,这与浙江GDP的快速增长是紧密相关的,本文对浙江保费收入与经济增长之间的相关性进行了研究分析。分析中还发现与全国情况差别最大的是,浙江保费收入对政策性因素并不敏感的。在文章的最后,对这种不敏感性的原因进行了分析。     

我国保费收入增长模型实证分析

文章利用计量经济学的方法对我国保险公司的保费收入进行评价 ,通过统计软件 SPSS对指标数据进行了实证分析 ,详细介绍了如何运用计量经济学建立保费收入增长的评价模型 ,并预测 2 0 0 2～ 2 0 0 6年的保费收入增长额     

相对熵方法下的指数保费信度理论

文章讨论了在相对熵方法下的风险保费的信度估计问题,即在指数保费原理下,把贝叶斯估计限定在经验估计的线性组合中,根据均方误差最小原则和相对熵最大原则得到了保费的信度估计的表达形式,从而推广了经典的信度理论.     

Optimal Estimating Equations with Applications to Insect Development Times

This paper proposes a general method of estimation when a function of data and parameters is to be modelled, which is most efficient in a certain sense. It is applied to the following situation. If X(t) is the temperature at time t, the time Y taken for an insect to complete a stage of growth is assumed to satisfy the model.     

交强险IBNR评估方法研究

由于中国交强险方案的调整,导致目前保监会规定的四种IBNR评估方法都不适合.所以,以中国某家财产险公司医疗费用为例,给出了一种新IBNR评估方法.该方法利用了损失数据信息,考虑了责任限额和费率调整所产生的影响,弥补了B-F法和S-B法的缺点.最后对该方法的应用性进行了总结,并提出了若干建议.     

A Flexible Method for Estimating the ROC Curve

In this paper we propose a flexible method for estimating a receiver operating characteristic (ROC) curve that is based on a continuous-scale test. The approach is easily understood and efficiently computed, and robust to the smooth parameter selection, which needs intensive computation when using local polynomial and smoothing spline techniques. The results from our simulation experiment indicate that the moderate-sample numerical performance of our estimator is better than the empirical ROC curve estimator and comparable to the local linear estimator. The availability of easy implementation is also illustrated by our simulation. We apply the proposed method to two real data sets.     

Estimating Marginal Effects in Accelerated Failure Time Models for Serial Sojourn Times Among Repeated Events

Recurrent event data are commonly encountered in longitudinal studies when events occur repeatedly over time for each study subject. An accelerated failure time (AFT) model on the sojourn time between recurrent events is considered in this article. This model assumes that the covariate effect and the subject-specific frailty are additive on the logarithm of sojourn time, and the covariate effect maintains the same over distinct episodes, while the distributions of the frailty and the random error in the model are unspecified. With the ordinal nature of recurrent events, two scale transformations of the sojourn times are derived to construct semiparametric methods of log-rank type for estimating the marginal covariate effects in the model. The proposed estimation approaches/inference procedures also can be extended to the bivariate events, which alternate themselves over time. Examples and comparisons are presented to illustrate the performance of the proposed methods.     

Accuracy Analysis of the Percentile Method for Estimating Non Normal Manufacturing Quality

We consider asymmetric kernel estimates based on grouped data. We propose an iterated scheme for constructing such an estimator and apply an iterated smoothed bootstrap approach for bandwidth selection. We compare our approach with competing methods in estimating actuarial loss models using both simulations and data studies. The simulation results show that with this new method, the estimated density from grouped data matches the true density more closely than with competing approaches.     

抽样调查中数据信号的半参数估计

文章提供了一个用来对抽样调查中的数据信号进行再处理的方法,该方法是滤波技术、信号提取技术以及半参数回归估计技术的一种综合。并对参数估计的结果进行了方差分析,数值模拟分析结果表明该方法是有效的。     

A Monte Carlo Method for Estimating Prediction Limit for the Arithmetic Mean of Lognormal Sample

A Monte Carlo (MC) method is suggested for calculating an upper prediction limit for the mean of a future sample of small size N from a lognormal distribution. This is done by obtaining a Monte Carlo estimator of the limit utilizing the future sample generated from the Gibbs sampler. For the Gibbs sampler, a full conditional posterior predictive distribution of each observation in the future sample is derived. The MC method is straightforward to specify distributionally and to implement computationally, with output readily adapted for required inference summaries. In an example, practical application of the method is described.     

农作物遥感估产方法介绍(上)

随着科技水平的发展,卫星遥感技术开始造福于民,然而许多人并不十分清楚到底什么是遥感,它的先进之处在哪、可以用它来做些什么。本刊特请国家统计局统计科学研究所空间统计研究室的同志就遥感技术在农作物估产方面的应用做一些普及性的介绍,将分两期予以刊登。     

我国各地区常住人口总量推算方法探讨

 本文以年度人口变动调查为基础,通过调查指标之间的关系、人口变动自身特征与抽查的情况,对我国各地区常住人口的推算方法进行了分析研究,并对各种评估推算方法的优缺点和适用条件进行了分析比较,根据各地区人口流动特点与人口基数,选择合适的评估推算方法,准确推算出各地常住人口数据。如何加强全国与各地数据衔接问题是今后推算评估数据工作的重点。     

A Novel Bayesian Parameter Mapping Method for Estimating the Parameters of an Underlying Scientific Model

Population-parameter mapping (PPM) is a method for estimating the parameters of latent scientific models that describe the statistical likelihood function. The PPM method involves a Bayesian inference in terms of the statistical parameters and the mapping from the statistical parameter space to the parameter space of the latent scientific parameters, and obtains a model coherence estimate, P(coh). The P(coh) statistic can be valuable for designing experiments, comparing competing models, and can be helpful in redesigning flawed models. Examples are provided where greater estimation precision was found for small sample sizes for the PPM point estimates relative to the maximum likelihood estimator (MLE).