bluuebunny/crossref_metadata_2025_split huggingface dataset:
Survey Error
Authors: Reiter Jerome P. 
Published: September 2014

Abstract
In survey samples, the primary objective usually is to estimate unknown population quantities, such as population means or totals. The estimates invariably do not exactly equal the population values, i.e. there are survey errors. This article reviews the main sources of survey errors and general approaches to reducing them.

Survey errors can be classified in two categories,
nonsampling
and
sampling
errors. Common sources of nonsampling error include measurement error, coverage error, mode effect error, nonresponse error, and process error. Measurement error occurs when respondents misinterpret, cannot answer, or inaccurately answer questions. Coverage error occurs when the list used to take the sample does not correspond to all units in the target population. Mode effect error occurs when the way the data are collected influences responses. Nonresponse error occurs when those who do not respond to a survey differ from those who respond to it. Process error occurs when edits, recodes, or other data processing steps go awry. Nonsampling errors are hard to detect and quantify. Their impact can be reduced by careful survey design. Sampling errors occur because only subsets of populations are measured, and estimates vary by sample. Sampling errors arise in every survey, but they are quantifiable.
HierarchicalMarkov ChainMonteCarlo (MCMC) forBayesian System Reliability
Authors: Wilson Alyson G. 
Published: September 2014

AbstractHierarchical models are one of the central tools of Bayesian analysis. They offer many advantages, including the ability to borrow strength to estimate individual parameters and the ability to specify complex models that reflect engineering and physical realities. Markov chain Monte Carlo (MCMC) is a set of algorithms that allow Bayesian inference in a variety of models. We illustrate hierarchical models and MCMC in a Bayesian system reliability example.
Component Reliability Importance
Authors: Rigdon Steven E. 
Published: September 2014

Abstract

The concept of reliability importance gives a measure of how much changes in the reliability of a particular component of a coherent system affects the reliability of the system. The definition of the importance of component
k
is
I
(
k
) = ∂
r
(

p

)/∂
p

k

, where

p

is the vector of component reliabilities and
r
(

p

) is the system reliability function. Examples of the reliability importance for common systems are given. The structural reliability importance is also given, along with its relationship to the reliability importance.
Reliability of Redundant Systems
Authors: Pham Hoang 
Published: September 2014

AbstractThis articles provides a brief concept of how to evaluate the reliability of redundant systems with various aspects including dynamic redundancy, imperfect coverage, fail‐safe redundancy, noncoherent systems, degradable redundancy, multiple failure modes,k‐out‐of‐nsystem redundancy, weighted systems, and load‐sharing redundancy.
Fault Trees
Authors: Bartlett Lisa M. 
Published: September 2014

Abstract
The ability to analyze how failures occur and determine their likelihood of occurrence is fundamental in maximizing the functionality of a system. To achieve this, a technique such as fault tree analysis is required. The technique can be used to carry out a qualitative analysis, namely, identifying the failure events that contribute to the system failure, and a quantitative analysis, calculating numerical statistics relating to the system, for example, the probability of failure. This article provides an introduction to the technique and its analysis potential.
System Reliability:
M
onte
C
arlo Estimation
Authors: Rigdon Christopher J. , Rigdon Steven E. 
Published: September 2014

Abstract

Monte Carlo methods are useful for a number of situations in reliability. First, for very complex systems, it is difficult to determine the system reliability even if the component reliabilities are known. This can be done by simulating the performance (operating/failed) of each component, and determining the status of the system given the performances of these components. Second, the system reliability can be estimated when component reliabilities are estimated from pass/fail data. Uncertainties in the resulting estimate of system reliability can be obtained
via
Monte Carlo simulation. Third, many questions of availability (or other measures of system performance) can be treated using queuing theory. Since queuing models can become intractable, especially when times between events or repair times are not exponential random variables, Monte Carlo methods can be used.
System Reliability: Computational Algebra Methods
Authors: Leemis Lawrence M. 
Published: September 2014

Abstract

Computer algebra systems are useful in calculating system reliability. We use several diverse examples to illustrate how a computer algebra system can be helpful in determining system reliability. The examples presented here are (a) analyzing a
k
‐out‐of‐
n
system; (b) determining the mean time to failure of a parallel system with symbolic parameters; (c) determining the conditional survivor function for a system with a piecewise survival distribution; (d) determining the probability of mission success for a unit and two cold standby units; (e) determining a lower confidence bound on the system reliability from binary failure data collected on each of the independent components in a coherent system
via
bootstrapping; and (f) determining the exact distribution of a popular goodness‐of‐fit test statistic.
Reliability, Safety, and Risk Management
Authors: Cox Sue 
Published: September 2014

AbstractThe aim of this paper is to present a brief exposition on the connection between safety and reliability. The author adopts an approach that is described in full in the bookSafety, Reliability and Risk Management: An Integrated Approach. This book was designed to support students and practitioners in the development of their understanding of safety and its links to reliability. The text is structured around a systems approach to safety, reliability, and risk management and highlights a number of practical tools that practitioners have used to identify and manage risk. This approach is compatible with the current development of risk management theory, both in relation to public decision making and loss prevention strategies.
Block Replacement
Authors: Dekker Rommert 
Published: September 2014

Abstract
Block replacement is the replacement of a block of components at fixed time intervals. Its aim is to prevent failures, which can have severe negative consequences, both with respect to economics and safety. It is appropriate if components exhibit an increasing failure rate. A main problem is to determine the optimal replacement time. For this purpose the block‐replacement model has been formulated. We discuss how the optimum replacement time can be computed and we compare block replacement with some other policies.
System Downtime Distributions
Authors: Pievatolo Antonio 
Published: September 2014

Abstract
System downtime distributions are useful for determining economic or safety performance measures of a system. We consider system downtime in an interval and after a failure. We try to give expressions that can be readily programmed using only information on failure and repair time distributions of every single component of the system. For this reason, all such expressions are of asymptotic nature. We deal with systems of independent binary components in detail, whereas we mention only briefly (also indicating references) the tools that can be used for systems with multistate components.
Repairable Systems:
B
ayesian Analysis
Authors: Rigdon Steven E. 
Published: September 2014

Abstract

The Bayesian approach to analyzing data from repairable systems is presented in this chapter. Specifically, the homogeneous Poisson process and the power law process, a nonhomogeneous Poisson process with intensity equal to λ(
t
) = (β/θ)(
t
/θ)
β−1
,
t
> 0, are studied. Other models, such as the renewal process, follow a similar pattern. Also, measures of system performance, such as availability, are discussed, where the Bayesian approach provides a convenient way to combine uncertainties. For multiple copies of a repairable system, we discuss empirical Bayes and hierarchical Bayes methods.
Queues in Reliability
Authors: Ausín M. Concepción 
Published: September 2014

Abstract
Queuing models can be useful in solving many complex reliability problems. Component failures are usually interpreted as the arrival of customers and the repair or replacement of failed components is typically associated with the service facility. A distinctive characteristic of queues in reliability is that requests for service are usually generated by a finite customer population because, in general, there are a limited number of units, e.g., machines which can fail, and when they are all in the system, being repaired or waiting for repair, no more can arrive. Thus the arrivals do not form a renewal process as they may depend on the number of units in the system. This is an essential difference from typical queuing systems, where the population of potential arrivals can be considered to be effectively limitless. This article gives an overview of the main queuing models used in reliability which are illustrated using the classical machine repairmen model. Some statistical methods to estimate the main quantities of interest in a queue are also discussed.
Reliability Allocation
Authors: Malaiya Yashwant K. 
Published: September 2014

Abstract
A system is generally designed as an assembly of subsystems, each with its own reliability attributes. The overall system reliability is a function of the subsystem reliability metrics. The cost of the system is the sum of the costs for all the subsystems. This article examines possible approaches to allocate the reliability values such that the total cost is minimized. The problem is very general and is applicable for mechanical systems, electrical systems, and computer hardware and software. The problem involves expressing the system reliability in terms of the subsystem reliability values and specifying the cost function for each subsystem, which allows setting up an optimization problem. Software reliability allocation is examined as a detailed example. The article also examines some preliminary apportionment approaches that have been proposed. In some cases, it is possible to use exact optimization methods. In general, a complex case will require use of iterative approaches.
S
ix
S
igma Method
Authors: Does Ronald J.M.M. , De Mast Jeroen 
Published: September 2014

Abstract
Six Sigma is built on principles and methods that have proven themselves over the twentieth century. It has incorporated the most effective approaches and integrated them into a full program. It offers a management structure for organizing continuous improvement of routine tasks, such as manufacturing, service delivery, accounting, nursing, sales, and other work that is done routinely. Further, it offers a method and tools for carrying out improvement projects effectively.
Dependent Risks
Authors: Denuit Michel , Dhaene Jan 
Published: September 2014

Abstract
In risk theory, all the random variables (r.v.s, in short) are traditionally assumed to be independent. It is clear that this assumption is made for mathematical convenience. In some situations however, insured risks tend to act similarly. In life insurance, policies sold to married couples involve dependent r.v.s (namely, the spouses' remaining lifetimes). Another example of dependent r.v.s in an actuarial context is the correlation structure between a loss amount and its settlement costs (the so‐called ALAE's). Catastrophe insurance (i.e. policies covering the consequences of events like earthquakes, hurricanes or tornados, for instance), of course, deals with dependent risks.
Adjustment Coefficient
Authors: Kaas Rob 
Published: September 2014

Abstract

The adjustment coefficient is the number
R
appearing in the famous Lundberg upper bound: in a compound Poisson risk process with initial capital
u
≥ 0, the ruin probability, ψ(
u
), is bounded by e

−
Ru

. By changing the safety loading or the distribution of the individual claims that are involved in its definition, the value of
R
and with it the ruin probability can be adjusted.
Cramér‐Lundberg Condition and Estimate
Authors: Yang Hailiang 
Published: September 2014

Abstract
This article surveys the main results and developments of Cramér–Lundberg approximation. We present an overview of the classical results under the Lundberg–Cramér model, and the corresponding results in the cases of heavy‐tailed claim distribution are discussed. Recent developments in the diffusion perturbed insurance risk models and in the model in which an α‐stable Levy process is added to the compound Poisson process are covered.
Early Warning Systems
Authors: Wong Johnny 
Published: September 2014

Abstract
An early warning system provides information to insurers for monitoring their financial stability and soundness, and to regulators for identifying those insurers who may become insolvent so that early remedial actions can be taken. It normally uses a solvency measurement system, which values the excess of assets over liabilities. A dynamic solvency measurement system, which evaluates the insurers' ability to remain solvent in the future under varying conditions, is preferred over a static solvency measurement system, which determines solvency at a particular valuation date. A good early warning system should also balance the interests of consumers, policyholders, insurers, and shareholders.
Risk Minimization
Authors: Møller Thomas 
Published: September 2014

AbstractRisk minimizationis a theory from mathematical finance that can be used for determining trading strategies. The theory of risk minimization was instituted by Föllmer and Sondermann who essentially suggested the determination of trading strategies for a given liability by minimizing the variance of future costs defined as the difference between the liability (or claim) and gains made from trading on the financial market.
Severity of Ruin
Authors: Tsai Cary Chi‐Liang 
Published: September 2014

Abstract
In this article, continuous‐time risk models are introduced; the time of ruin, and the surplus immediately before and at ruin caused by a claim are defined. Then the probability of ruin, the (discounted) moment of the severity of ruin caused by a claim, the joint moment of the severity of ruin and the time of ruin caused by a claim, and the (discounted) defective joint/marginal distribution and probability functions of the surplus immediately before and at ruin are studied.
Time of Ruin
Authors: Tsai Cary Chi‐Liang 
Published: September 2014

Abstract
In this article, both classical discrete‐ and continuous‐time risk models are introduced; then the time of ruin and the severity of ruin are defined. The probability of ruin and some quantities concerning the time of ruin, such as the expectation of the present value of the time of ruin, the (discounted) moment of the severity of ruin caused by a claim, the joint moment of the severity of ruin, and the time of ruin caused by a claim, and the moments of the time of ruin due to oscillation and claim, respectively, are studied.
Assets in Pension Funds
Authors: Bowie David 
Published: September 2014

AbstractIn countries with large scale private and public funded pension arrangements, for example, the United States, Canada, Japan, the Netherlands, and the United Kingdom, one of the key decisions is how the contributions into the fund should be invested to best effect. The investment decision typically results in some form of risk sharing between members and sponsor in terms of (1) the level of contributions required to pay for all promised benefits, (2) the volatility of contributions required, and (3) the uncertainty of the level of benefits actually deliverable should the scheme be wound up or have to be wound up. Some of the risks associated with the pension benefits have a clear link with the economy and hence with other instruments traded in the financial markets. Others, such as demographic risks and the uncertainty as to how members or the sponsor will exercise their options, which are often far from being economically optimal, are less related to the assets held in the fund.This article describes broadly what assets are available to the institutional investor, how an investor might go about deciding on an asset allocation, and explore what the possible consequences of an asset allocation might be.
Health Insurance
Authors: Bernet Patrick M. , Getzen Thomas E. 
Published: September 2014

AbstractThe health insurance industry differs from most other forms of insurance in that virtually all of the insured persons expect to make claims in most years, and those at highest risk expect to have their losses subsidized by the government or other parties. In this paper, the premium pricing process is used to highlight the many special features of health insurance with particular attention to frequency of loss, post‐loss price negotiation, adverse selection, tax benefits, social insurance, IBNR and reserve policies.
Life Insurance Mathematics
Authors: Norberg Ragnar 
Published: September 2014

Abstract
The subject matter and methodology of modern life insurance mathematics are surveyed. Standard insurance products with payments depending only on life history events are described and analyzed in the commonly used Markov chain model under the assumption of deterministic interest rates. The actuarial equivalence principle for determining premiums and reserves is motivated by risk diversification in large portfolios. Differential equations for reserves and other conditional expected values are obtained and proclaimed basic constructive and computational tools. The traditional method for managing nondiversifiable risk arising from changes in, for example, mortality rates and interest rates is outlined; premiums are calculated under prudent assumptions and systematic surpluses thus created are repaid as bonus. An alternative way of managing nondiversifiable risk is to link the contractual payments to the development of mortality, interest, and possibly other demographic or economic indexes. Examples of so‐called index‐linked products are given, and premiums and reserves are determined by combined use of classical actuarial principles and principles of pricing and hedging from financial mathematics. Transfer of nondiversifiable risk to the financial markets through creation of tradable insurance derivatives is outlined as an idea. Methods for estimation of mortality and other vital rates, formerly a major issue in life insurance mathematics, are fetched from modern statistical life history analysis and therefore only briefly described.
Long‐Term Care Insurance
Authors: Jones Bruce L. 
Published: September 2014

Abstract
It is well known that older adults become increasingly frail as they age. Even in the absence of a disease, one eventually loses the physical and/or mental ability to live independently. The kind of assistance required when this occurs is referred to as long‐term care (LTC). LTC is very different from the health care needed by individuals with injury or disease, and it requires a different set of services. Often, long‐term care services can be provided in a person's home, and this is the ideal situation. However, many cases require that the individual be institutionalized in an appropriate LTC facility.
Pensions: Finance, Risk and Accounting
Authors: MacNicol Roger 
Published: September 2014

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.
Present Values and Accumulations
Authors: Macdonald Angus S. 
Published: September 2014

Abstract

Money has a
time value
; if we invest $1 today, we expect to get back more than $1 at some future time as a reward for lending our money to someone else who will use it productively. Suppose that we invest $1, and a year later we get back $(1 +
i
). The amount invested is called the
principal
, and we say that
i
is the
effective rate of interest per year
. Evidently, this definition depends on the time unit we choose to use. In a riskless world, which may be well approximated by the market for good quality government bonds,
i
will be certain, but if the investment is risky,
i
is uncertain, and our expectation at the outset to receive $(1 +
i
) can only be in the probabilistic sense.

We can regard the accumulation of invested money in either a retrospective or prospective way. We may take a given amount, $
X
say, to be invested now and ask, as above, to what amount will it accumulate after
T
years? Or, we may take a given amount, $
Y
say, required in
T
years' time (to meet some liability perhaps) and ask, how much we should invest now, so that the accumulation in
T
years' time will equal $
Y
? The latter quantity is called the
present value
of $
Y
in
T
years' time. For example, if the effective annual rate of interest is
i
per year, then we need to invest $1/(1 +
i
) now, in order to receive $1 at the end of one year. In standard actuarial notation, $1/(1 +
i
) is denoted
v
, and is called the discount factor. It is immediately clear that in a deterministic setting, accumulating and taking present values are inverse operations.
Profit Testing
Authors: Richards Stephen 
Published: September 2014

Abstract
Every business needs to know if its products are profitable. However, it is much harder to determine the profitability of long‐term life and pensions business than most other industries. Strictly speaking, one only knows if a tranche of business has been profitable when the last contract has gone off the books, which could take fifty years for certain kinds of contract. Clearly, some methodology is needed to assess likely profitability before writing the business in the first place. This article is devoted to the subject of profit testing, the process of assessing the profitability of an insurance contract in advance of it being written.
Surplus in Life and Pension Insurance
Authors: David O'Brien Christopher 
Published: September 2014

AbstractSurplus in life insurance is a key issue for participating business: actuarial control over the emergence and distribution of surplus is a major subject in the management of life insurance companies. Although we here concentrate on surplus in the context of participating life business, surplus is also relevant in any kind of fund in which long‐term liabilities may be valued on the basis of assumptions about the future. For example, shareholders have needs and expectations of surplus in nonparticipating life business, with premium rates and payouts being determined with a view to rewarding shareholders, who have provided capital and run risks. In pensions business there are similar issues concerning the sources, size, analysis, and uses of surplus.