在线等一下三段英语的翻译 麻烦各位英语好的大神帮帮忙啊

在线等一下三段英语的翻译 麻烦各位英语好的大神帮帮忙啊
Everyday we face all kinds of risks, and insurance is in the business of
providing us a means to transfer or share these risks, usually to eliminate or reduce
the resulting financial burden, in exchange for a predetermined price or tariff. Actuaries
are considered professional experts in the economic assessment of uncertain
events, and equipped with many statistical tools for analytics, they help formulate a
fair and reasonable tariff associated with these risks. An important part of the process
of establishing fair insurance tariffs is risk classification, which involves the grouping
of risks into various classes that share a homogeneous set of characteristics allowing
the actuary to reasonably price discriminate. This article is a survey paper on the statistical
tools for risk classification used in insurance. Because of recent availability
of more complex data in the industry together with the technology to analyze these
data, we additionally discuss modern techniques that have recently emerged in the
statistics discipline and can be used for risk classification. While several of the illustrations
discussed in the paper focus on general, or non-life, insurance, several of
the principles we examine can be similarly applied to life insurance. Furthermore,
we also distinguish between a priori and a posteriori ratemaking. The former is a
process which forms the basis for ratemaking when a policyholder is new and insufficient
information may be available. The latter process uses additional historical
Stimulated by a study in Bangladesh about the first birth interval, we
propose a semivarying-coefficient model for cluster data analysis. We consider the
estimation procedure for the proposed model and establish the asymptotic results of
the proposed estimators. Furthermore,we employ the cross-validation (CV) to identify
the constant coefficients. The associated asymptotic properties are rigorously examined.
Simulation studies are conducted to investigate the performance of the proposed
estimation and the CV-based model selection procedure for finite sample size. Finally,
our methods are used to analyse the aforementioned data set to explore how several
factors affect the first birth interval in Bangladesh.
Estimators based on the mode are introduced and shown empirically to
have smaller Kullback–Leibler risk than the maximum likelihood estimator. For one
of these, the midpoint modal estimator (MME), we prove the Kullback–Leibler risk is
below 1
2 while for the MLE the risk is above 1
2 for a wide range of success probabilities
that approaches the unit interval as the sample size grows to infinity. The MME
is related to the mean of Fisher’s Fiducial estimator and to the rule of succession for
Jefferey’s noninformative prior

Everyday we face all kinds of risks,and insurance is in the business of
providing us a means to transfer or share these risks,usually to eliminate or reduce the resulting financial burden,in exchange for a predetermined price or tariff.
我们每天都面临着各种风险,而保险则是致力于为我们提供一种转移或者分摊这些风险的方式,通过交付一定的费用,最终达到消除或降低金融负担的目的.
Actuaries are considered professional experts in the economic assessment of uncertain events,and equipped with many statistical tools for analytics,they help formulate a fair and reasonable tariff associated with these risks.
在对不确定因素方面进行经济评估中,精算师是专家,有众多数据分析工具进行分析,由此对这些风险形成了一个公正合理的价目表.
An important part of the process of establishing fair insurance tariffs is risk classification,which involves the grouping of risks into various classes that share a homogeneous set of characteristics allowing the actuary to reasonably price discriminate.
为确立公正的保险价目表,重要步骤之一便是将各类风险进行风险归类---具有相同特征的风险划归在同一个类别中,以便精算师做区别定价.
This article is a survey paper on the statistical tools for risk classification used in insurance.
本文着力研究保险行业进行风险归类所使用的数据分析工具.
Because of recent availability of more complex data in the industry together with the technology to analyze these data,we additionally discuss modern techniques that have recently emerged in the statistics discipline and can be used for risk classification.
鉴于保险业目前有更为复杂的资料及技术来分析这些数据,我们还讨论了当前已融入数据领域的可用于风险归类分析的技巧.
While several of the illustrations discussed in the paper focus on general,or non-life,insurance,several of the principles we examine can be similarly applied to life insurance.
尽管本文中讨论的几种举例着重于一般的、非人寿保险,我们核实的数条原则可以直接运用于人寿保险.
Furthermore,we also distinguish between a priori and a posteriori ratemaking.The former is a process which forms the basis for ratemaking when a policyholder is new and insufficient information may be available.The latter process uses additional historical
此外,我们也将事前评级与事后评级区别而论.事前评级是保险客户刚确立业务关系且只有部分信息明晰时的一种基础评级,而事后评级则是争对老客户及历史的额外信息.
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