bluuebunny/crossref_metadata_2025_split huggingface dataset:
Liability Insurance
Authors: Tess Daniel 
Published: September 2006

Abstract
Liability insurance generally provides cover for financial loss, which arises as a result of a finding of liability under law. Many types of specialized insurance products have developed which covers certain types of liability or specific situations in which liability can arise. Some products, which are often grouped together with liability insurance do not cover liability per se, but rather cover the cost of defined benefits to injured persons in lieu of liability awards.
Life Table Data, Combining
Authors: Tolley H. Dennis , Fellingham Gilbert W. 
Published: September 2006

AbstractThis article discusses methods of combining data from several life tables to form an overall life table. Combining life tables entails building a statistical model using the data from the various tables. The simplest statistical model is to assume that the probabilities of decrement for each age are the same across all tables. In this case, combining entails simple addition. When this assumption is untenable, a more complex statistical model must be built. This article illustrates how to combine life table data using a specific family of statistical models. Other candidate models for this step are mentioned at the end.
L
loyd's
Authors: Michaelson Robin 
Published: September 2006

Abstract
To understand Lloyd's, one has to realize what it is not. It is not an insurance company with shareholders or a mutual, nor does it accept liability for a risk as a trading entity under the name of ‘Lloyd's’. It is, in fact, a ‘society’, or group of individual and corporate members, who accept a share of a risk by operating through an underwriting syndicate, in which the individual members (called Names) are liable to the full extent of their personal private wealth, to supply the capital backup to meet the insurance liabilities, but the corporate organizations operate with a form of limited liability.
Logistic Regression Model
Authors: Dierckx Goedele 
Published: September 2006

AbstractLogistic regression is a very flexible tool to study the relationship between several explanatory variables and a binary response. To fit and examine such a logistic regression model, some tools used with generalized linear models, as introduced by McCullagh and Nelder in 6, are useful. Once the model is fitted and the goodness‐of‐fit has been examined, the relationship between covariates and the response can be better understood by interpreting the coefficients in the logistic model in terms of odds ratios. The model can also be used to make predictions. As such, the logistic model provides a method to classify objects in two (or more) groups as well.
Loss Ratio
Authors: Babcock Nathan J. 
Published: September 2006

Abstract
Actuaries and numerous other insurance professionals use loss ratios in the measurement of profitability, the determination of appropriate pricing and reserving levels, the creation of business strategies, and the completion of financial reports. A loss ratio expresses the relationship between one of several financial items, typically insured losses, and premiums. There are many types of data from which loss ratios can be calculated. The data can include paid or incurred losses and earned or written premium; the data also can be organized by line of business, by any of several accounting period types, by insurance/reinsurance layer, or by evaluation date.
L
undberg Inequality for Ruin Probability
Authors: Yang Hailiang 
Published: September 2006

Abstract
This article surveys the main results and developments of Lundberg inequality for ruin probability. We briefly describe various insurance risk models in the literature, as well as the Lundberg inequality for ruin probability under these models. Finite time horizon insurance risk model and the effect of the invest income are discussed. We also mention some main results, including the Lundberg type inequality, for the severity of ruin, the distribution of surplus before ruin, and the joint distribution of surplus before and after ruin.
M
arkov Chains and
M
arkov Processes
Authors: Jacobsen Martin 
Published: September 2006

Abstract
In many applications, the usual assumption of independent and identically distributed random variables has to be abandoned. Markov dependence is a first possibility. We deal with Markov chains in discrete and continuous time. We then extend the setting to Markov processes on more general state spaces. We pay special attention to stationary processes and to piecewise deterministic Markov processes where the behavior of the process between jumps is basically deterministic.
Dynamic Financial Modeling of an Insurance Enterprise
Authors: Hardy Mary R. 
Published: September 2006

Abstract
A dynamic financial insurance model is an integrated model of the assets and liabilities of an insurer. In the United Kingdom, these models may be called ‘model offices’. Modeling the insurer as a whole entity differs from individual policy or portfolio approaches to modeling in that it allows for interactions and correlations between portfolios, as well as incorporating the effect on the organization of decisions and strategies, which depend on the performance of the insurer as a whole. For example, profit distribution is most sensibly determined after consideration of the results from the different insurance portfolios that comprise the company, rather than at the individual portfolio level. The insurance enterprise model takes a holistic approach to asset–liability modeling, enabling synergies such as economies of scale, and whole insurer operations to be incorporated in the modeling process.
Nonexpected Utility Theory
Authors: Machina Mark J. 
Published: September 2006

AbstractAlthough the expected utility model of risk preferences has long been the most widely used model of choice under uncertainty, a growing body of evidence has uncovered systematic departures from expected utility maximization. Researchers have characterized the form of these departures, developed several models of nonexpected utility risk preferences, and have applied these models to the analysis of standard questions such as the theory of insurance.
Participating Business
Authors: O'Brien Christopher David 
Published: September 2006

AbstractIn participating life business, also referred to as ‘with profits’, there are policyholders with rights to share in the surplus (or profits) of the business. This means there are important issues in the management of such business, including how to price policies that contain such rights, how to determine the investments of the fund (given that the obligations to policyholders depend on the surplus that is earned), and how to assess the amount of surplus to be distributed. There is then the question of how to share surplus between the participating policyholders and, in a stock life insurer, the shareholders.
Pensions: Finance, Risk and Accounting
Authors: MacNicol Roger 
Published: September 2006

Abstract
This article deals with the financing of defined benefit pension schemes. The financing of defined contribution schemes is much simpler and can be summarized as follows. An amount is paid into a scheme for each member. That amount will be determined by market forces (what other companies, competing for the same workers, are paying) and by what a company can afford. Where specific benefit targets are used within defined contribution schemes, they take on some of the characteristics of defined benefit arrangements and their financing will follow, to a greater or lesser extent (depending on the type of benefit guarantee given) the principles below.
Defined benefit pensions can be financed in a number of ways. At one extreme, ‘Pay As You Go’ (PAYG) systems do not involve any prefunding (i.e. the accumulation of funds during employees' working lifetimes) whatsoever. At the other extreme, an immediate lump sum might be set aside, its amount being sufficient (on an appropriate set of actuarial assumptions) to meet all future pension outgo. This approach is very unlikely to be used in practice.
Premium Principles
Authors: Young Virginia R. 
Published: September 2006

AbstractIn this review article, we discuss how premium principles arise in theory and in practice. We propose three methods that actuaries use to derive premium principles: (1) thead hoc methodin which the actuary simply proposes a (potentially) reasonable premium principle then studies its properties; (2) thecharacterization methodin which the actuary lists the properties that she wants the premium principle to satisfy, then finds (all) the premium principles that satisfy those properties; and (3) theeconomic methodin which the actuary espouses an economic theory and determines the resulting premium principle. These methods are not exclusive, as we note in the article.
Random Number Generation and Quasi‐MonteCarlo
Authors: L'Ecuyer Pierre 
Published: September 2006

AbstractProbability theory defines random variables and stochastic processes in terms ofprobability spaces, a purely abstract notion whose concrete and exact realization on a computer is far from obvious. (Pseudo) random number generators (RNGs) implemented on computers are actually deterministic programs, whichimitateto some extent, independent random variables uniformly distributed over the interval [0,1] (i.i.d.U[0,1], for short). RNGs are a key ingredient for Monte Carlo simulations, probabilistic algorithms, computer games, cryptography, casino machines, and so on.
Random Variable
Authors: Asmussen Søren 
Published: September 2006

Abstract

In intuitive terms, a random variable (r.v.)
X
is a number, whose value is determined by random factors (chance). For example, the exchange rate Euro/USD next New Year or the number of car accidents in a certain month and county.

In the car accident example, the month may be in the past or in the future. In the first case,
X
is then completely known, say
X
= 738, and rather than as a r.v., one speaks of 738 as the
outcome
of the r.v.
X
. The precise interpretation of such issues in terms of the axioms for probability theory is discussed.

A r.v. is usually assumed to be real‐valued,
X
∈ Υ, like in the above examples. However, in principle also, other sets of values are possible. The most prominent example is multivariate r.v.s,
X
= (X
1
,…, X
d
) ∈ Υ
d
(e.g. the set of exchange rates in January,
d
= 31), which are discussed in detail in this article.

Random variables are one of the most fundamental concepts of probability theory and treated in any elementary or intermediate textbook.
Risk Classification, Pricing Aspects
Authors: Feldblum Sholom 
Published: September 2006

Abstract
The cost of insurance coverage depends on the attributes of the insured: males have higher mortality than females and young male drivers have higher accident rates than young female drivers. Insurance premiums are based on expected losses and vary similarly. Differentiation among risks by expected cost is termed risk classification.
Risk classification uses the characteristics of a group to set rates for its members. Certain forms of classification are prohibited in many jurisdictions, such as classification by race or ethnic group; other types of classification, such as applicant age for life insurance or type of firm for workers' compensation insurance, are commonly accepted as the proper basis for insurance rates.
A good risk classification system has several attributes, not all of which are essential. Much debate focuses on the importance of each attribute; actuaries and economists emphasize the equity and homogeneity attributes, whereas other groups may emphasize more the intuition, incentives, or social acceptability attributes.
Risk Management: An Interdisciplinary Framework
Authors: Tapiero Charles S. 
Published: September 2006

Abstract
Risk management is shown to be based on both theory and practice. It is shown to be conceptual and technical, blending behavioral psychology, financial economics, and decision‐making under uncertainty into a coherent whole that justifies the selection of risky choices. Its applications are broadly distributed across many areas and fields of interest. The examples treated here have focused on both finance and insurance and on a few problems in industrial management.
Robustness
Authors: Hubert Mia , Rousseeuw Peter J. , Van Aelst Stefan 
Published: September 2006

AbstractWhen applying a statistical method in practice, it often occurs that some observations deviate from the usual assumptions. However, many classical methods are sensitive to outliers. The goal of robust statistics is to develop methods that arerobust against the possibility that one or several unannounced outliers may occur anywhere in the data.
Social Security
Authors: Eriksen Tor 
Published: September 2006

AbstractSocial security is a concept that can be interpreted in many different ways and should therefore be used with the utmost caution.Barr notes that in the United States, it refers to retirement pensions; in the United Kingdom, it refers to the entire system of cash benefits; and in mainland Europe (in accordance with the practice of the International Labor Organization), it refers to all cash benefits plus health care.Barr chooses to describe the security system by examining the purpose of the various cash benefits.Universal benefits awarded on the basis of a specified event, without regard to a person's income or contribution record. Examples include family allowance and free health care.Social assistance awarded on the basis of an income test and the occurrence of a specified event, without a contribution test.Consequently, social security can be seen as an accumulative concept for different systems that provide economical compensation in the case of illness, unemployment, old age, and so on.
Stochastic Orderings
Authors: Denuit Michel , Müller Alfred 
Published: September 2006

Abstract
We describe the basic ideas of stochastic orders, which formally describe intuitive ideas like ‘being larger’, ‘being more variable’ or ‘being more dependent’ for risks or for portfolio of risks. We give the definitions of the most important stochastic orders like usual stochastic order, convex order, supermodular order and others, and we consider their most important properties and some applications.
Stop‐Loss Reinsurance
Authors: Rytgaard Mette M. 
Published: September 2006

Abstract
Stop loss is a nonproportional type of reinsurance protecting the annual result of the ceding company. Stop loss is, under certain conditions, the optimal form of reinsurance for the ceding company but not optimal to the reinsurer. Considerations to be made by the reinsurer before accepting stop‐loss covers are listed. An overview of various classes of methods to price reinsurance is given.
S
undt's Classes of Distributions
Authors: Vernic Raluca 
Published: September 2006

Abstract
Computing the aggregate claims distribution is generally not an easy task. Among various methods used for such computations, recursive methods have the advantage of giving accurate values with considerable savings in the computing time. The purpose of this article is to give an overview of the classes of counting distributions introduced by Sundt 8, these classes leading to recursive evaluation of the corresponding aggregate claims distributions.
Reinsurance Supervision
Authors: Boller Hans Peter , Haegin Thomas 
Published: September 2006

Abstract
The discussion of reinsurance regulation and supervision falls into a period where heightened regulatory vigilance and uncertainty can be observed in the entire financial services industry. It is widely accepted today, that there is a need for a globally recognized method of conducting reinsurance regulation. At the international level the ambition is to create a system of common and shared standards under which the world's reinsurers will, through mutual recognition, be able to trade globally without having to comply with the different regulatory demands of national and regional authorities. This would contribute to a more efficient and transparent global market place.
Time Series
Authors: Franke Jürgen 
Published: September 2006

AbstractA(discrete) time seriesis a sequence of dataxt,t= 1, …,N(real numbers, integer counts, vectors,…), ordered with respect to timet. It is modeled as part of the realization of a stochastic process…,X-1,X0,X1,X2, …, that is, of a sequence of random variables, which is also called a time series. In engineering applications, complex‐valued time series are of interest, but in economic, financial, and actuarial applications, the time series variables are real‐valued, which we assume from now on. We distinguish betweenunivariateandmultivariatetime series for one‐dimensionalXtand for vector‐valuedXtrespectively. For the sake of simplicity, this article considers mainly univariate time series.
Transforms
Authors: Grübel Rudolf 
Published: September 2006

Abstract

The use of transforms is one of the pervasive ideas in mathematics and the sciences. From a pragmatic point of view, a transformation can be regarded as a tool for simplifying an otherwise difficult operation, for example, a differential equation can be turned into a simple algebraic equation with the help of Laplace transforms. Even the formula, log(
x
·
y
) = log
x
+ log
y
, in connection with the traditional use of logarithmic tables and slide rules may be seen in this light. Transforming an object may also provide insight into its structure, for example, writing a periodic function as a superposition of sine waves. From a theoretical point of view, transforms of the type considered in this article are at the core of major mathematical theories such as harmonic analysis; they have initiated research that spans the whole range of contemporary mathematics.
Unit‐Linked Business
Authors: Richards Stephen 
Published: September 2006

Abstract
This article is about unit‐linked insurance business, including unitised with‐profits (UWP) business. The major distinguishing feature of unit‐linked business is that a separate and explicit asset account is maintained for each policy. Each policy can therefore be invested entirely independently of the others, and this investment flexibility is under the control of the policyholder, not the insurer. The value of a unit‐linked policy is directly linked to the investment performance of the unitised funds selected by the policyholder. Unlike traditional with‐profits business, there is no element of discretion for setting bonuses for pure unit‐linked business. An important consequence of this for the insurer is that expenses and charges are explicitly separated, and the simple ‘cost‐plus’ approach to managing pricing has to be abandoned in favor of profit testing.
W
ilkie Investment Model
Authors: Hardy Mary R. 
Published: September 2006

Abstract
The Wilkie stochastic investment model, developed by Professor Wilkie, is described fully in two papers; the original version was published in 1986 and the model was reviewed, updated, and extended in 1995. In addition, the latter paper includes much detail on the process of fitting the model and estimating the parameters. It is an excellent exposition, both comprehensive and readable. It is highly recommended for any reader who wishes to implement the Wilkie model for themselves, or to develop and fit their own model.
The Wilkie model is commonly used to simulate the joint distribution of inflation rates, bond yields, and returns on equities. The 1995 paper also extends the model to incorporate wage inflation, property yields, and exchange rates. The model has proved to be an invaluable tool for actuaries, particularly in the context of measuring and managing financial risk. In this article, we will describe fully the inflation, equity, and bond processes of the model. Before doing so, it is worth considering the historical circumstances that led to the development of the model.
Working Covers
Authors: Patrik Gary 
Published: September 2006

Abstract
A working cover is a very standard and prevalent form of excess‐of‐loss treaty reinsurance. Working covers provide capacity and stabilization for a ceding company's financial results. A working cover provides capacity because it allows the ceding company to write higher policy limits and yet maintain a manageable risk level. Working covers enhance competition within the insurance marketplace by providing a means by which smaller insurers can compete with larger insurers. Working covers are often large enough so that many of the pricing parameters can be estimated either directly from the exposure and claims history or by a credibility weighting of that history with more general industry information.
Fluorescence Imaging
Authors: Ramjiawan Bram , Jackson Michael , Mantsch Henry 
Published: September 2006

Abstract
A number of fluorescence imaging techniques show diagnostic promise. Imaging endogenous fluorescence has been proposed as a method for cancer diagnosis. Unfortunately, tissue autofluorescence is relatively weak and poor contrast between malignant and normal tissue is seen. Contrast may be enhanced with the addition of fluorescent materials that are selectively accumulated by malignant cells, such as fluoroscein or porphyrin derivatives. The limited penetration of light at the emission maxima of these materials restricts the use of fluorescence techniques utilizing these chromophores to superficial phenomena. However, many potential applications still exist. For example, monitoring fluorescence during surgery may allow resection margins to be clearly delineated.
Other exogenous chromophores that may have may have diagnostic utility include indocyanine green (ICG). Techniques based upon visualization of the distribution of ICG fluorescence (i.e. choroidal angiography) are already prominent in ophthalmology. ICG fluorescence imaging may also find a useful niche in monitoring of burns and transplant tissue. In addition, monitoring of vascular parameters during cardiac surgery presents exciting opportunities. For example, low oxygen levels (e.g. during bypass surgery) in the heart can result in alterations in microvascular permeability. As ICG is largely bound to serum albumin, it should not be seen in extravascular spaces in normal hearts. However, increased permeability will allow albumin to diffuse into the extravascular spaces, and diffuse fluorescence across the surface of the heart will be seen. In principle, the infusion of polymers (e.g. dextrans) of various molecular weights labeled with dyes that fluoresce at various wavelengths will allow the assessment of the porosity of capillary beds in such systems.
Immunofluorescence techniques have the potential to provide unmatched sensitivity and specificity. The unique nature of antibody–antigen interactions ensures specific delivery of the fluorophore to the site of interest. The specific interaction of labeled antibodies with antigens means that the fluorophore persists in the body for a prolonged period of time (days). Following a single injection of labeled antibody, repeated measurements on the same site over the course of hours or days allow kinetic information to be readily obtained. In principle, this means that the effect of therapeutic intervention, i.e. radiation therapy, chemotherapy, etc., can be monitored.