bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deregulation of Commercial Insurance
Authors: Klein Robert 
Published: September 2014

Abstract
This article utilizes the US experience as a primary point of reference with some discussion of insurance deregulation in other countries. The concept of insurance deregulation contemplates a shifting of regulatory emphasis from prior approval of commercial lines rates and forms to a more competitive regulatory system coupled with more intensive regulatory monitoring of market competition and strategic intervention when required. It is important to stress that this does not involve an abrogation of regulatory oversight, but rather a restructuring of regulatory functions to facilitate more efficient and competitive markets combined with an array of regulatory activities, tools, and authorities to support strategic, proactive regulatory intervention if problems develop.
Frontier between Public and Private Insurance Schemes
Authors: Zweifel Peter 
Published: September 2014

Abstract
From an economic point of view, insurance coverage is very much like any other goods or service purchased by households and firms. One would therefore expect individually contracted insurance coverage provided by private firms to dominate. However, mandated coverage provided by public insurance is common for the provision for old age, disability, workers' compensation, health, long‐term care and unemployment. Contributions to public insurance amount to about 30% of the Gross Domestic Product (GDP) and are still rising in major industrialized countries whereas the premium revenue of personal lines of private insurance make up as little as 5% of GDP even in the United Kingdom, a stronghold of private insurance. This raises the question, what determines the frontier between public and private insurance? and, in a normative vein, would a shifting of this frontier be in the interest of the insured?
Insurability
Authors: Gollier Christian 
Published: September 2014

Abstract
We first describe the competitive equilibrium of frictionless insurance markets with symmetric information. Risks are insurable in this economy in the sense that all mutually advantageous risk exchanges are realized at equilibrium. We then provide various reasons why many risks cannot be efficiently shared in our decentralized economy. Transaction costs, adverse selection, moral hazard, insurance fraud, and the inexistence of an objective distribution function do generate an insurability problem that requires public intervention. The heterogeneity of beliefs and the difficulties in diversifying individual risks are other reasons why some risks are not insurable, but this does not mean that insurance markets are inefficient.
Pareto Optimality in Insurance
Authors: Dana Rose‐Anne 
Published: September 2014

Abstract
Any sequence of positive, independent and identically distributed random variables can be considered as a renewal process. Traditional examples are the interclaim times and the successive claims in a homogeneous portfolio. The article describes the most important quantities and results from renewal theory. Also defective, delayed and discrete‐type renewal processes are discussed and illustrated with examples from actuarial science.
Pooling Equilibria
Authors: Boyer Marcel , Martin Boyer M. 
Published: September 2014

Abstract
We present in this paper, different contexts under which pooling equilibria emerge in insurance economics. A pooling equilibrium may emerge when it is not optimal (or not feasible or not allowed) to offer different contracts to different agents. As a result, agents with different risk types are ‘pooled’ together even though the low‐risk agents end up subsidizing the high‐risk agents. This subsidy, quite like an externality imposed by the high‐risk types on the low‐risk ones, may lead low‐risk insureds to try to identify themselves and insurers to search for different ways to bypass the pooling equilibrium by identifying and luring away good risk types, thereby destabilizing the pooling equilibrium.
Black–Scholes Model
Authors: Henderson Vicky 
Published: September 2014

AbstractThe Black–Scholes model was the first, and is the most widely used model for pricing options. The model and associated call and put option formulas have revolutionized finance theory and practice, and the surviving inventors Merton and Scholes received the Nobel Prize in Economics in 1997 for their contributions.Black and Scholes and Merton introduced the key concept of dynamic hedging whereby the option payoff is replicated by a trading strategy in the underlying asset. They derive their formula under log‐normal dynamics for the asset price, allowing an explicit formula for the price of European call and put options.Remarkably their formula (and its variants) have remained the industry standard in equity and currency markets for the last 30 years. Despite the assumptions underpinning the formula being somewhat unrealistic, options prices computed with the formula are reasonably close to those of options traded on exchanges. To some extent, the agreement in prices may simply reflect the fact that the Black–Scholes model is so popular.
Retrospective Premium
Authors: Meyers Glenn 
Published: September 2014

Abstract
Insurance is about the transfer of risk. If an entity, either a person or a business, wants to transfer all of its risk of loss to an insurer and the insurer is able to quantify the risk that the entity wishes to transfer, the price of the risk transfer is readily established by market forces and a deal is struck.
In many cases, the insurer is not confident that it can quantify the risk that the entity wishes to transfer. An entity sensing difficulty in getting a full transfer of risk may be willing to settle for a partial transfer of risk. The insurer would be more comfortable with a partial risk transfer because the entity has an incentive to keep its loss at a minimum.
This article describes a particular kind of risk‐sharing agreement called a retrospective rating plan. In these plans, the premium for an insurance contract is a function of the actual loss paid under the contract. The final premium is not known until after the contract has expired. This explains the name retrospective rating.
Pooling in Insurance
Authors: Aase Knut K. 
Published: September 2014

Abstract
Risk sharing resulting in pooling of risk is considered. We take the perspective of Pareto optimal risk sharing, originating from a model introduced by Karl Borch in the late 1950s, and discuss when pooling may result, and also what is needed for pooling not to be optimal. Finally, we illustrate by some examples, including mutual insurance schemes, and also a limited liability partnership.
Reinsurance, Reserving
Authors: Radtke Michael 
Published: September 2014

Abstract
The article deals with actuarial reserving techniques used in reinsurance. The impact of reinsurance on the actuarial reserving approach is discussed with a focus on the actuarial methods. In reinsurance, very often, the standard methods, that is, Chain‐Ladder or Bornhuetter Ferguson have to be modified because of specific reinsurance requirements. We introduce some methodical adjustments and illustrate the practical circumstances under which they have been taken into account. The paper ends with some comments on practical applications of reserving methods in reinsurance.
Cochrane, Archibald ('Archie') Leman
Authors: Elwood P. , Hughes C. 
Published: September 2014

Abstract
Born in 1909 in Galashiels, UK; died in 1988 in Dorset, UK. Cochrane is best known for his dedication to improving epidemiological methodology, and for being instrumental in establishing the Cochrane Collaboration, which facilitates access to the results of health care interventions.
H
aenszel,
W
illiam
M
Authors: Levy Paul S. 
Published: September 2014

Abstract

Born 1910; died 1998. Although William M. Haenszel is probably best known as the name to the right of the hyphen on the Mantel–Haenszel test and Mantel–Haenszel odds ratio, he made other equally important contributions throughout a very long and productive career as a biostatistician and epidemiologist. Of particular importance was his leadership in establishing and maintaining the population‐based cancer registry known as
SEER
(for
S
urveillance
E
pidemiology and
E
nd
R
esults); his comparative studies of cancer occurrence in foreign‐born and native‐born Japanese Americans; and his use and advocacy of biomarkers and pathological findings in studies of cancer etiology, especially those involving gastric and large bowel cancer. His work from the 1950s to 1970s at the National Cancer Institute was at the cutting edge of chronic disease epidemiology, and greatly enriched that field both substantively and methodologically.
I
rwin,
J
oseph
O
scar
Authors: Armitage Peter 
Published: September 2014

Abstract
After periods of work under Karl Pearson and R. A. Fisher, Oscar Irwin joined the staff of the Medical Research Council in 1931 and thereafter became the leading mathematical statistician in British medical research. He had a wide range of theoretical interests, including the use of the characteristic function, the development of the exact test for the 2 × 2 table, and the Waring distributions. His biometric interests included bioassay, factor analysis, accident proneness, and carcinogenicity experiments.
P
earl,
R
aymond
Authors: Dixon Dennis O. 
Published: September 2014

Abstract
Raymond Pearl (1879–1940) worked in Michigan as a biologist until 1905. After a year's study in London with Karl Pearson, he returned to the US and increasingly worked in areas of human biology. In 1918, he became the first Professor of Biometry and Vital Statistics in the Johns Hopkins School of Hygiene and Public Health, Baltimore, and started a prolific period of research in a wide range of human biology, including public health, eugenics and heredity. A major line of research was human population growth.
Popper, Karl R
Authors: Fagot‐Largeault A. 
Published: September 2014

Abstract

Born 1902; died 1994. Famous as a philosopher of science, Popper maintained that scientific truth is not manifest, but that indeed that scientific theories are conjectures open to refutation or falsification. Popper controversially denounced inductive reasoning—validity of a scientific theory from empirical observations to general regularities—insisting instead that scientific theories should be open to testing. As a theory passes more severe tests, it becomes more highly “corroborated”. He called this approach
hypothetico‐deductive
. ‘Realism and the Aim of Science’ (1982) is his work most relevant to biostatistics, re‐examining in detail problems of truth, corroboration, and probability.
Y
ule,
G
eorge
U
dny
Authors: F. Edwards A. W. 
Published: September 2014

Abstract

G.U. Yule (1871–1951) worked from 1892 at University College London under Karl Pearson, and from 1912 to 1931 at Cambridge. He published
An Introduction to the Theory of Statistics
in 1911, the 11th and subsequent editions of which were coauthored by M.G. Kendall. He contributed to the theory of regression, correlation, genetics, epidemiology, and time series, and developed a coefficient of association for 2 × 2 tables. He described what is now usually known as Simpson's paradox for association in a 2 × 2 × 2 tables. He introduced the simple birth process in the study of evolution.
Binomial Distribution
Authors: Wypij David 
Published: September 2014

Abstract

The binomial distribution refers to the number of successes in
n
independent trials with a common success probability
p
in each. With large
n
, the distribution is approximated by the normal, and with large
n
and small
p
, it is approximated by the Poisson. This article reviews its properties and discusses point estimation, confidence interval estimation, and hypothesis testing for the parameter
p
. Extensions are also briefly considered.
Chi‐Squared Tests ‐ Further Developments
Authors: Koehler Kenneth 
Published: September 2014

AbstractA chi‐square test investigates whether observed data are consistent with a proposed probability model. Best‐known chi‐square statistics are the Pearson statistic and the likelihood‐ratio statistic. This article also mentions various other statistics that have limiting chi‐square distributions, all of which are special cases of the power divergence statistic. The article begins by introducing simple goodness‐of‐fit tests, then discusses a variety of chi‐square statistics. Chi‐square tests are then presented that apply with fixed intervals or with random intervals. Applications are then discussed for categorical data, such as testing independence. Extensions are discussed for small samples and sparse tables, and for correlated responses and complex surveys.
Contingency tables
Authors: E. Altham P. M. 
Published: September 2014

Abstract
Contingency tables give counts for cross classifications of categorical variables. This article illustrates two‐dimensional tables (two variables). It then discusses the hypothesis of independence and presents chi‐squared tests. An example of quasi‐independence is presented, and small‐sample tests are mentioned. Extensions to multiway contingency tables and the relevant hypotheses for such tables are also discussed. Loglinear models describe the hypotheses, and there are corresponding logistic regression models when one of the variables is a binary response variable.
Iterative Proportional Fitting
Authors: Speed Terry P. 
Published: September 2014

Abstract
Iterative proportional fitting, also known as iterative proportional scaling, is an algorithm for constructing tables of numbers satisfying certain constraints. In its simplest form, the algorithm enables one to construct two‐way tables with specified row and column margins and prescribed association, measured by a family of cross‐ratios. The algorithm can be used to obtain maximum likelihood estimates of table entries under hierarchical loglinear models, and it can be used to adjust counts in survey sampling, where it is known as raking. Natural extensions exist to three and higher‐way tables, and to more general exponential families.
Logistic Regression, Conditional
Authors: W. Hosmer David , Lemeshow Stanley 
Published: September 2014

Abstract
This section describes the conditional logistic regression model. This model is appropriate when one wishes to model a binary outcome variable with matched or highly stratified data and when one is not interested in matched sets or stratum effects. The resulting fitted model may be used to estimate odds ratios in the same manner as is done with the usual logistic regression model. Examples of estimation and interpretation are given.
Mantel–Haenszel Methods
Authors: Landis J. Richard , Sharp Tonya J. , Kuritz Stephen J. , Koch Gary G. 
Published: September 2014

AbstractThis article provides an overview of generalized Mantel–Haenszel (MH) methods for the analysis of categorical data from factor–response and repeated measures study designs. These methods are illustrated using data from two different clinical research studies, investigating treatment differences within several different sets of 2 × 2 tables in a clinical trial, and within‐subject differences in an ordinal response across ordinal factor levels within a repeated measures design. The underlying multiple hypergeometric probability structure, based on a randomization model framework for hypothesis testing, is summarized for testing alternative hypotheses of 1) general association; 2) mean responses differ; and 3) linear trend in mean responses. These generalized MH methods can all be implemented directly within SAS and StatXact, with appropriate stratification and choice of scores.
Matched Pairs with Categorical Data
Authors: Davis Charles S. 
Published: September 2014

AbstractMatched pairs with categorical data arise when two measurements of the same categorical variable are obtained from each experimental unit. Such data are generally displayed in a two‐way contingency table. When the variable of interest is binary (dichotomous), marginal homogeneity can be tested using McNemar's test or the exact binomial one‐sample test. For a polytomous variable, marginal homogeneity can be tested using Stuart's test, and by tests proposed by Bhapkar and Madansky. The likelihood ratio test of marginal homogeneity conditional on the assumption of quasi‐symmetry can also be used. The hypothesis of symmetry for a polytomous variable can be tested using Bowker's test and the likelihood ratio test. For ordered categorical variables, one degree of freedom tests of marginal homogeneity can be carried out using tests analogous to those of Stuart and Bhapkar. Another approach to testing marginal homogeneity for ordered classifications is based on the conditional symmetry loglinear model.
Proportions, Inferences and Comparisons
Authors: Connor Jason T. , Imrey Peter B. 
Published: September 2014

AbstractThis article selectively reviews asymptotic, exact, and Bayesian methods for inference about one proportion, or for comparing two proportions from separate samples. For a single proportion, interval estimation is emphasized, and coverage properties of Wald, Jeffreys, Wilson, Agresti–Coull, and Clopper–Pearson (exact) confidence intervals are compared. Hypothesis testing is also discussed, primarily in the context of inverting interval estimates. For comparing proportions between two samples, we consider tests including the power‐divergence family (which incorporates all asymptotic tests commonly used); both conditional and unconditional exact tests; Bayesian tests; and several interval estimates. Some general aspects of power computation and sample size determination are discussed briefly in both contexts.
Quasi‐Independence with Biomedical Examples
Authors: Mcdonald John W. 
Published: September 2014

AbstractThe quasi‐independence model is a generalization of the model of independence for a two‐dimensional contingency table. It states that a particular subset of cells satisfies the independence structure whereby the cell expected count is a product of a row effect and a column effect. It is often used for square tables (number of rows equal number of columns), the relevant subset being all cells not falling on the main diagonal. This article also discusses connections with loglinear models, extension to multidimensional tables, inference and testing of model fit, and applications and relationships with other models.
Quasi‐symmetry
Authors: Hatzinger Reinhold 
Published: September 2014

Abstract
In its simplest form, quasi‐symmetry is a model for square contingency tables that states that certain odds ratios are symmetric around the main diagonal. A special case is complete symmetry, by which probabilities themselves are symmetric around the main diagonal. Another special case is quasi‐independence, by which the odds ratios equal 1. This is equivalent to quasi‐symmetry for 3 × 3 tables. This article discusses its multiplicative formula, gives an example, discusses generalizations to higher‐dimensional tables, discusses tests related to complete symmetry and marginal symmetry, and mentions related models.
Two‐By‐Two Table: Overview
Authors: Haber Michael 
Published: September 2014

Abstract

This article summarizes the most commonly used statistical techniques for analyzing two by two frequency tables. It presents methods for the estimation of measures of association (risk ratios, risk differences, odds ratios) and agreement (kappa). Tests of hypotheses are described for large samples (based on asymptotic theory) and for small samples (exact tests). Both conditional (Fisher's exact test) and unconditional exact tests are discussed, and the controversy regarding the use of conditional versus unconditional tests is summarized. The article advocates the use of the mid
p
‐value exact test for small sample sizes. Methods for the determination of sample sizes for tests on two by two tables are also described.
Decision Analysis in Diagnosis and Treatment Choice
Authors: A. Tosteson Anna N. 
Published: September 2014

Abstract
Decision analysis, a quantitative approach to decision making under conditions of uncertainty, is introduced and its use in clinical decision making is described. Decision analysis identifies the optimal course of action as that which maximizes (or minimizes) the expected value of the outcome as specified by the decision maker. Through sensitivity analysis, the parameters that affect the optimal course of action are identified. The expected value of clinical information and perfect information are defined in the context of a clinical example.
Observer Reliability and Agreement
Authors: Vet H. 
Published: September 2014

AbstractThe termsobserver reliabilityandobserver agreementrepresent different concepts. Reliability coefficients express the ability to differentiate between subjects. Agreement parameters determine whether the same value is achieved if a measurement is repeated. Parameters for reliability are ICCs for measurements on an interval scale. Parameters for agreement are kappa for measurements on a nominal scale, weighted kappa for an ordinal scale, and levels of agreement for measurements on an interval scale.Assessing observer reliability and agreement is essential for the interpretation of clinical observations in research and medical practice. Moreover, only if the sources of measurement errors are known, can they be anticipated, thereby improving the quality of measurements.
Computerized Therapeutic Decision Support
Authors: Pifer Eric , Weiner M. G. , Williams Sankey V. 
Published: September 2014

AbstractThe increasing breadth and depth of clinical information stored electronically has enabled computerized therapeutic decision support systems to provide appropriate and timely advice to providers, which that is tailored specifically to a patient's clinical status. This chapter provides a taxonomy for therapeutic decision support systems in terms of the goals of the advice, the mechanism in which the advice is given, the information needed to generate the advice, and the underlying reasoning used. Examples of applications of therapeutic decision support systems are provided with an assessment of their utility within the complex processes of clinical care.
Software for Clinical Trials
Authors: Arena Vincent C. , Rockette Howard E. 
Published: September 2014

AbstractThis chapter provides a comprehensive overview of software that is relevant to the design, management, and analysis of clinical trials. Guidance is provided on selecting programs appropriate to the needs of the researcher. There are useful suggestions on keeping software up to date. An extensive table is included referencing most of the available software packages with brief descriptions of the features most relevant to clinical trials, vendor names, contact information, and web URLs.
Sample Size Determination in Survival Analysis
Authors: Collett D. 
Published: September 2014

Abstract
This article outlines how the sample size for a study can be determined when the response variable is a survival time. This is a two‐stage process in which the number of events required to meet prespecified power requirements is first determined. The occurrence of censoring generally means that not everyone recruited to a study will experience the end point, and so the next step is to calculate the number of individuals who need to be recruited to give the required number of events. This number will depend on parameters such as the anticipated recruitment rate and the follow‐up period. The procedure is illustrated using a numerical example.