bluuebunny/crossref_metadata_2025_split huggingface dataset:
Financial Intermediaries: The Relationship between their Economic Functions and Actuarial Risks
Authors: Booth Philip 
Published: September 2014

Abstract
The purpose of this article is to explain the economic functions of financial intermediaries and financial institutions. The risks that are commonly managed by actuaries, particularly in the insurance industry, (e.g. insurance risk, market risk, interest‐rate risk etc.) arise from the economic functions of financial institutions and from the process of financial intermediation. We show that there are substantial similarities between the economic functions of nonbanks (in which actuaries have commonly managed risk) and those of banks (in which risk has generally not been managed by actuaries – at least not in the UK and US). The implication of the analysis is that risk management techniques will become closer in the banking and nonbanking sectors and that the various risk management professions and academic groups will cross the sectors.
Financial Markets
Authors: Nitzsche Dirk 
Published: September 2014

Abstract

Financial markets provide the antithesis of Polonius' advice to his son in Shakespeare's
Hamlet
, of ‘neither a borrower or lender be’. Individuals benefit not only from consuming goods and services that are primarily produced by firms but also by the government sector (e.g. education, health, and transport facilities in many countries). To produce ‘goods’, firms and the government often have to finance additional current and capital investment expenditures by borrowing funds. Financial markets and institutions facilitate the flow of funds between surplus and deficit units.
Market Models
Authors: Pelsser Antoon 
Published: September 2014

AbstractInterest rate modelling is one of the crucial ingredients in the calculation of the market value of insurance products. The standard case is the LIBOR Market Model where all interest rates have log‐normal distributions. We deal with LIBOR rates and options on LIBOR rates (caplets and floorlets) first, then with swap rates and options on swap rates (swaptions).
Derivative Pricing, Numerical Methods
Authors: Vetzal K.R. 
Published: September 2014

Abstract
Numerical methods are needed for derivatives pricing in cases where analytic solutions are either unavailable or not easily computable.
The subject of numerical methods in the area of derivatives valuation and hedging is very broad. A wide range of different types of contracts are available, and in many cases there are several candidate models for the stochastic evolution of the underlying state variables. Many subtle numerical issues can arise in various contexts. A complete description of these would be very lengthy, so in this article we will only give a sample of the issues involved.
The article first presents a detailed description of the different methods available in the context of the Black – Scholes – Merton model for simple European and American‐style equity options. It then describes how the methods can be adapted to more general contexts, such as derivatives dependent on more than one underlying factor, path‐dependent derivatives, and derivatives with discontinuous payoff functions.
Risk Measures
Authors: Albrecht Peter 
Published: September 2014

AbstractThe literature on risk is vast (the academic disciplines involved are, for instance, investment and finance, economics, operations research, management science, decision theory, and psychology). So is the literature on risk measures. This contribution concentrates on financial risks and on financial risk measures (this excludes the literature on perceived risk (the risk of lotteries perceived by subjects) the literature dealing with the psychology of risk judgments and also the literature on inequality measurement.
Bonus‐Malus Systems
Authors: Lemaire Jean 
Published: September 2014

Abstract
Bonus‐malus is the most common form of a posteriori rating in automobile third‐party liability insurance. The policyholder moves around the classes of a Markov chain according to his annual number of claims. Claim‐free years are rewarded by premium discounts or bonuses; at‐fault accidents are penalized by surcharges called maluses. The different principles underlying the design and evaluation of bonus‐malus systems are reviewed.
Catastrophe Models and Catastrophe Loads
Authors: Meyers Glenn 
Published: September 2014

Abstract
Natural catastrophes such as earthquakes, hurricanes, tornadoes, and floods have an impact on many insurers, and the accumulation of losses to an insurer can jeopardize the financial well‐being of an otherwise stable, profitable insurer. Hurricane Andrew, in addition to causing more than $16 billion in insured damage, left at least 11 companies insolvent in 1992. The 1994 Northridge earthquake caused more than $12 billion in insured damage in less than 60 seconds.
Fortunately, such events are infrequent. But it is exactly their infrequency that makes the estimation of losses from future catastrophes so difficult. The scarcity of historical loss data makes standard actuarial techniques of loss estimation inappropriate for quantifying catastrophe losses. Furthermore, the usefulness of the loss data that does exist is limited because of the constantly changing landscape of insured properties.
It is these two aspects of catastrophic losses – the possibility that a quick accumulation of losses can jeopardize the financial well‐being of an insurer, and the fact that historical loss information provides little help in estimating future losses – that this article addresses.
Ratemaking
Authors: McClenahan Charles L. 
Published: September 2014

Abstract
The goal of the ratemaking process is the establishment of actuarially sound rates for property and casualty insurance, or other risk transfer mechanisms. Because ratemaking generally requires that rates be established prior to the inception of the insurance, a rate is ‘an estimate of the expected value of future costs’. Such rates are actuarially sound if they provide for all of the expected costs of the risk transfer and appropriately reflect identified individual risk characteristics.
Many state rating laws in the United States require that insurance rates be ‘not inadequate, excessive or unfairly discriminatory’. If a rate is actuarially sound, it is deemed to meet these regulatory requirements without regard to the extent to which the actuarial expectations are consistent with the actual developed experience.
This article will introduce and discuss the basic elements of property and casualty actuarial ratemaking. The examples and illustrations have been substantially simplified, and it should be noted that the methods presented herein are but a few of the methods available and may not be appropriate for specific actuarial applications.
Financial Reinsurance
Authors: Halliwell Leigh J. 
Published: September 2014

Abstract
The article presents ‘financial reinsurance’ as a continuum between finance and reinsurance. It discusses features of finance and reinsurance, illustrates simple combinations of the two, and describes basic financial‐reinsurance contracts. It concludes with the benefits and dangers of financial reinsurance, its accounting treatment, and the need to define risk transfer.
Largest Claims and
ECOMOR
Reinsurance
Authors: Beirlant Jan 
Published: September 2014

Abstract

There exist a few intuitive and theoretically interesting reinsurance contracts that are akin to excess‐of‐loss treaties, but refer to the larger claims in a portfolio. An unlimited excess‐of‐loss contract cuts off each claim at a fixed, nonrandom retention. A variant of this principle is ECOMOR reinsurance where the priority is put at the (
r
+ 1)th largest claim so that the reinsurer pays that part of the
r
largest claims that exceed the (
r
+ 1)th largest claim. Compared to unlimited excess‐of‐loss reinsurance, ECOMOR reinsurance has an advantage that gives the reinsurer protection against unexpected claims inflation if the claim amounts increase, then the priority will also increase. A related and slightly simpler reinsurance form is largest claims reinsurance that covers the
r
largest claims but without a priority.

The article highlights some of the theoretical results that can be obtained for the case where the total volume in the portfolio is large. In spite of their conceptual appeal, largest claims type reinsurance are rarely applied in practice. The discussion hints at some of the possible reasons for this lack of popularity.
Stop‐Loss Reinsurance
Authors: Rytgaard Mette M. 
Published: September 2014

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.
B
liss,
C
hester
I
ttner
Authors: Holford Theodore R. , White C. 
Published: September 2014

Abstract
Born in 1899 in Springfield, OH; died in 1979. Bliss is best known for work on probit (probability integral transformation) analysis, much of it in collaboration with R.A. Fisher, and also for playing a major role in founding the International Biometric Society.
B
radley,
R
alph
A
Authors: Billard Lynne 
Published: September 2014

Abstract
Ralph Bradley's contributions to the world of statistics fall under two headings: his statistical research (especially the Bradley–Terry test used extensively in taste‐testing experiments) and his professional leadership role in statistical science, as evidenced by his development of statistical programs, by his presidency (1981) of the American Statistical Association, and by his editorial efforts.
C
halmers,
T
homas
C
lark
Authors: Wright E. 
Published: September 2014

Abstract
Born in 1917 in Forest Hills, NY; died in 1995 in Hanover, NH. Chalmers was a leader in the design, conduct, and evaluation of clinical trials, favoring randomized controlled trials in all areas of medicine, with independent policy advisory committees. He helped found the Society for Clinical Trials.
G
reenwood,
M
ajor
Authors: Wilkinson Lise 
Published: September 2014

Abstract
Major Greenwood is known principally for the important role he played in the development of biostatistics during the first half of the twentieth century, both as an epidemiologist and as a pioneer medical statistician. Having qualified in medicine in 1904, he turned to medical research rather than practice, influenced by the work of the physiologist (Sir) Leonard Hill and by Karl Pearson's teaching of biological statistics at University College London. From then on, he developed the methodology of medical statistics to make it ultimately acceptable to an initially reluctant medical profession, adding good clinical judgment to Pearson's rigorous mathematics. A few weeks before his sudden death in London during a meeting on cancer research, he argued the case for the Voluntary Euthanasia Society in a debate broadcast by the BBC.
Kolmogorov, Andrey Nikolayevich
Authors: Suhov Yurii 
Published: September 2014

AbstractKolmogorov (1903–1987), who worked in the Moscow State University, was one of the greatest mathematicians of the twentieth century. In probability theory, which was merely one of his fields of study, he introduced axioms and clarified various basic concepts. In statistics, he introduced the Kolmogorov–Smirnov statistic for testing goodness of fit, and worked on problems in estimation theory. His work in population genetics involved stochastic processes.
M
ahalanobis,
P
rasanta
C
handra
Authors: Ghosh J. K. , Majumder Partha P. 
Published: September 2014

Abstract
Mahalanobis (1893–1972) became involved with the analysis of anthropologic data in the 1920s and developed his measure of generalized distance in 1930. In the Indian Statistical Institute, he made important theoretical and practical contributions to sample surveys and became Chairman of the United Nations Sub‐Commission on Statistical Sampling.
M
oran,
P
atrick
A
lfred
P
ierce
Authors: Wilson Susan R. 
Published: September 2014

Abstract
Pat Moran was an Australian statistical scientist with an enormous breadth and depth to his research interests. During his prolific career, Moran made substantial contributions to population genetics, medical statistics (particularly psychiatry), geometric probability, mathematical statistics, time series, and applied probability. He received numerous scientific honors, and now is commemorated by two Australian awards for young research statisticians.
Pearson,Karl: His Life and Contribution to Statistics
Authors: Magnello M. E. 
Published: September 2014

AbstractKarl Pearson (1857–1936) was the founder of biometrics andBiometrikaand a principal architect of the mathematical theory of statistics. He was a polymath, with a wide interest in intellectual and social pursuits, and first taught applied mathematics at University College London. He started to teach statistics and to develop new methods during the 1890s. He developed measures of correlation and discovered the chi‐ square distribution and used it for tests of goodness of fit and association in contingency tables. He established and ran four laboratories and established the first degree course in mathematical statistics in Britain. He worked closely with Galton and Weldon, and eugenics and heredity remained central interests throughout his life.
Bayesian Methods for Contingency Tables
Authors: Albert James H. 
Published: September 2014

AbstractBayesian alternatives to frequentist methods for fundamental problems in categorical data analysis, such as testing independence in two‐way contingency tables and estimating probabilities of categories containing no observations, have been considered for at least four decades. Since the 1980s, Bayesian methods of model selection have also seen extensive and continuing development. These attempt to address the tendencies of frequentist approaches to arrive at overly complex models, and to disregard the effects of variability in model selection on model‐based estimated effects and predictions. This entry briefly reviews several Bayes and empirical Bayes approaches to estimating probabilities of categories, testing equiprobability in a one‐way table, testing independence in a two‐way contingency table, and selecting among generalized linear models, with particular attention to loglinear models, for tables of higher dimension.
Binary Data
Authors: Collett D. 
Published: September 2014

AbstractWhen a response variable can take one of two possible forms, such as alive or dead, the resulting data are said to be binary. The focus of this article is on models for binary response variables, and the use of logistic regression models is emphasized. The models can be fitted using most statistical software packages. Alternative models can be compared using the deviance, and for grouped binary data, this statistic may be used as a measure of goodness offit. Techniques for assessing model adequacy are outlined and the use of these methods is illustrated with an example.
Capture–Recapture
Authors: Chao Anne 
Published: September 2014

AbstractCapture–recapture methods are used to estimate population sizes and related parameters such as survival rates, birth rates, and migration rates. A typical capture–recapture experiment places traps in the study area and samples the population several times. At each trapping sample, one records and attaches a unique tag to every unmarked animal, records the capture of any animal that has been previously tagged, and returns all animals to the population. This article describes the history of the method, discusses applications to epidemiology, discusses potential models, and discusses the challenges inherent in estimating the population size well.
Contagious Distributions
Authors: Kemp Adrienne W. , Kemp C. D. 
Published: September 2014

Abstract
Polya proposed a model for the spread of an infection, whereby the probability of catching a disease depended on the existing number of cases. This leads to a distribution that has a limiting negative binomial form. Neyman proposed an alternative approach that arises from a Poisson distribution of clumps, with the number of cells for each clump being Poisson. This article surveys probability distributions and corresponding generating functions that result from such constructions.
Correlated Binary Data
Authors: Pendergast Jane F. , Gange Stephen J. , Lindstrom Mary J. 
Published: September 2014

AbstractCorrelated binary data may arise in longitudinal or spatial studies or through some other form of clustering of sampling units. The simplest setting, with clusters of size two and no covariates, leads to familiar methods of analysis. Models with covariates include response feature models, conditionally specified models, transitional models, marginal models, and cluster‐specific models. These may lead to different interpretations of the regression parameters.
Beta‐Binomial Distribution
Authors: Rosner Bernard 
Published: September 2014

Abstract
The beta‐binomial distribution results from a beta distribution mixture of binomials. It is useful for clustered binary data, for which the ordinary binomial distribution is usually inappropriate because different observations are correlated rather than independent. Models using this distribution permit the mean to depend on explanatory variables. An implicit assumption of the distribution is that subunits are exchangeable within a cluster. Generalizations of it allow for data structures with multiple levels of nesting.
Chi‐Square Distribution Properties
Authors: Klotz Jerome 
Published: September 2014

Abstract
This article complements the introductory article on the chi‐square distribution, with details of its derivation as the distribution of the sum of squares of standard normal deviates, the density being a gamma function, and the relationship between its distribution function and that of the Poisson distribution. When the component normal distributions have nonzero mean, the sum of squares has a noncentral chi‐square distribution, depending on a noncentrality parameter, the sum of squares of the normal means. This plays a role in determining the power of a chi‐square test.
Normal Values of Biological Characteristics
Authors: Harris Eugene K. 
Published: September 2014

AbstractThis entry focuses on clinical biochemistry and hematology measurements, this being the area where most progress has been made in the estimation of normal values. Other examples are noted in passing. Parametric and nonparametric methods to estimate normal ranges are described, together with the identification and treatment of outlying observations. Calculation of normal ranges for population subgroups is discussed. There is a brief review of the establishment of time‐dependent normal values, and estimation of multivariate normal indexes or regions for correlated clinical variables.
Time Trade‐Off Technique
Authors: Gafni Amiram 
Published: September 2014

AbstractDecisions about medical treatments and the setting of health programs involve both technical and value judgments. An important one is evaluating potential tradeoffs between quality of life and length of life or between different domains of quality of life resulting from different treatment options. The time trade‐off TTO) technique is one of the methods used for that purpose. Its most prominent use is in generating the preference weights to be used in the QALY (quality adjusted life years) calculation. In this section, we briefly describe the technique and its underlying assumptions.
Cost–Benefit Analysis, Willingness to Pay
Authors: O'Brien Bernie 
Published: September 2014

Abstract
Cost–Benefit Analysis (CBA) is a technique of economic evaluation, where the benefits of programs are valued in monetary terms to permit direct comparison with program costs. There are a number of measurement and statistical challenges that arise while attempting to assign money values to health program benefits such as reduction in the risks of morbidity and mortality. The basic measurement goal is to quantify the trade‐offs that consumers are willing to make between reduced health risk and money. Specifically, to identify the maximum willingness to pay (WTP) for health benefits. In this chapter, we review several approaches to both market‐based “revealed preference” and survey‐based “stated preference” WTP estimation. A prevalent method developed in environmental economics is to ask respondents to accept/reject questions for increasingly higher monetary amounts (“bids”) and then to use survival‐type methods to estimate a bid curve, with covariates, that can be used to solve mean or median WTP.
Blinding or Masking
Authors: Day Simon J. 
Published: September 2014

Abstract
The need for blinding in clinical trials and epidemiological studies is reviewed and its importance stressed. Various reasons for blinding include the potential for patient bias, bias in patient management and evaluation, bias in data management and study management. Blinding and unblinding interim analyses need special attention, and procedures for emergency unblinding need to be available.
Four levels of blinding are described from the slightly ambiguous term “single‐blind” through to quadruple‐blinding. Methods to maintain the blind at various stages such as use of placebos, “double‐dummy” designs, and sham procedures are discussed.
Finally, some of the difficulties of blinding are highlighted. Often, it may be ineffective and reporting of the achieved quality of blinding is recommended. There are special issues with regard to equivalence studies, and regulatory guidance exists discussing many issues included in this article.
Data and Safety Monitoring
Authors: O'Brien Peter C. 
Published: September 2014

AbstractWe consider the role of a Data and Safety Monitoring Board (DSMB) in overseeing the conduct of a randomized clinical trial. This includes insuring both the scientific integrity of the study and patient safety, monitoring the occurrence of adverse events and assessing efficacy. Formal stopping rules are particularly useful in clinical trials for monitoring efficacy, with a view towards terminating the trial early if the evidence for or against efficacy becomes overwhelming prior to the scheduled completion of the trial. The most commonly used approaches in formal stopping rules are discussed in some detail. These include the Pocock and O'Brien–Fleming boundaries and the spending function approach developed by Lan and DeMets. Discussion of stochastic curtailment, point and interval estimation associated with early termination, multiple endpoints, and a brief overview of decision theoretic methods (including Bayesian methods) is provided.
Data Monitoring Committees
Authors: Demets David L. 
Published: September 2014

Abstract
Randomized clinical trials are the standard by which new interventions are evaluated for benefit or harm. Federal guidelines in the United States now recommend that all trials have a monitoring plan for early evidence of harm. For trials that have serious irreversible outcomes such as death, heart attack, stroke, or cancer recurrence, an external independent committee called a Data Monitoring Committee (DMCs) is recommended to evaluate the accumulating interim data. Trials for which interim data are convincing for benefit or harm may be terminated early. DMCs began in the early 1970s by trials sponsored by the National Institutes of Health and more recently by the pharmaceutical industry. DMCs review not only the primary outcome data and the serious adverse events but also the recruitment progress, patient compliance to the intervention, other secondary outcome variables, and high risk subgroups of patients if such exist. Because of the complexity of such monitoring, DMCs have a composition with biological, clinical, and biostatistical expertise.