bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reliability Growth Modeling
Authors: Sen Ananda 
Published: September 2014

Abstract
At the initial developmental phase of any production involving complex systems, a test‐analyze‐and‐fix (TAAF) process is often undertaken to promote the detection of system faults and incorporate appropriate redesigns or other corrective actions, and the modified system is retested. As this test–redesign–retest sequence contributes to an improvement in the system performance, failure data become increasingly sparse at the later stages of testing, making it more difficult to assess the current reliability. A reliability growth model provides a structure through which failure data from the current as well as previous stages of testing could be analyzed in an integrated way in order to make efficient inference on reliability, and other process parameters. Reliability growth models have been applied extensively in analyzing failure data arising from hardware as well as a piece of software. Further, for systems with single‐shot missions that undergo a developmental program, reliability growth models arise quite naturally while studying the effectiveness of design fixes. In this article, we describe some popular reliability growth models that have been developed in the context of observing either time to failure or sequence of polytomous outcomes.
Warranty Cost Prediction Based on Warranty Data
Authors: Karim M. Rezaul , Suzuki Kazuyuki 
Published: September 2014

Abstract
This article discusses methods for predicting warranty cost to the manufacturer during a given future value(s) of the variable(s) defining the warranty terms. It models the predicted cost as a function of the lifetime distribution of the product and the average cost per claim based on observed costs of previous claims or costs from previous model years.
Warranty Servicing
Authors: Murthy D.N. Prabhakar , Nat Jack 
Published: September 2014

Abstract
Warranty costs are the additional expenses that a manufacturer incurs to service all the claims made by customers. These costs can be reduced by performing effective waranty servicing. In this article, a framework is given to link all the issues involved in warranty servicing and models that a manufacturer can use to make optimal servicing decisions are received.
Accelerated Life Tests: Classical Methods for Design and Analysis
Authors: Giorgio Massimiliano 
Published: September 2014

Abstract
Accelerated life tests are used to quickly obtain information about the failure time distribution of very reliable technological products. Tests are conducted in conditions more severe than the normal operating ones to accelerate the mechanism of failure and compress the life of components and materials of interest.
An estimate of the lifetime distribution at normal use conditions is extrapolated from data, obtained at higher stresses, through the use of appropriate statistical models that incorporate a special relationship accounting for the effect of stress conditions on specific lifetime characteristics of the products under study.
After a brief introduction that describes motivations and objectives of accelerated testing, the following topics are addressed:

methods for accelerating reliability tests;

models for classical accelerated tests;

procedures for point estimation, interval estimation, and checking model assumptions; and

basic criteria for planning accelerated life tests.

In this article only the classical approach to design and analysis of accelerated constant‐stress tests will be discussed. Moreover only fully parametric acceleration models are considered.
Masked Failure Data
Authors: Yashchin Emmanuel 
Published: September 2014

Abstract

We discuss life‐testing situations in which some of the failures are not identified as being due to a single cause, but are rather explained by one of the causes in a group. Failures of this type are called
masked
. Within this framework, we discuss the problems of modeling, estimation, and diagnostics. Such problems involve, for example, identification of survival curves for individual causes, or estimation of parameters related to conditions under which masking occurs. Another class of problems is related to the impact of masking on root‐cause analysis of individual failures. We also discuss the impact of masking on quality and reliability management issues.
Accelerated Life Tests:Bayesian Models
Authors: Soyer Refik , Erkanli Alaattin , Merrick Jason R. 
Published: September 2014

AbstractIn this article we consider accelerated life test models where the effect of accelerated environments on failure behavior is described by time transformation functions. We present extensions of basic models by introducing hierarchical and dynamic strategies to allow for changes in the time transformation function with the accelerated environment. We develop Markov chain Monte Carlo methods to make Bayesian inferences from these models where no analytical forms are available for the posterior and predictive distributions of interest. We illustrate our approach with two examples and discuss what type of insights can be obtained from Bayesian analysis.
Single Sampling by Attributes and by Variables
Authors: von Collani Elart , Göb Rainer , Suresh Kodakanallur Krishnaswamy 
Published: September 2014

Abstract
Single sampling plans excel among statistical product inspection procedures. They are simple in structure, intuitive, and easy for implementation. The study of single sampling plans is illustrative for the understanding of acceptance sampling since single sampling plans exemplify all basic features of acceptance sampling in a particularly simple and transparent instance. Single sampling plans are sometimes theoretically inferior to more refined sampling algorithms like double, multiple, and sequential sampling plans. However, simplicity and ease in implementation have made single sampling the most widely used statistical product inspection procedure.
Variables Sampling Schemes in International Standards
Authors: Balamurali Saminathan , Göb Rainer , Jun Chi‐Hyuck 
Published: September 2014

Abstract

Variables sampling schemes are based on the specification range quality model: a specification range
R
is prescribed for a continuous measurement characteristic like weight, length, volume, and an item is considered as conforming, if the associated measurement
X
is in
R
. Issued in 1957, MIL‐STD‐414 was the first industrial standard on variables sampling. On the international level, the International Standards Organization (ISO) introduced in 1989, the first variables sampling standard ISO 3951, compatible with the attributes sampling standard ISO 2859, which is a civilian counterpart of MIL‐STD‐105E. ISO 3951 has been adopted as a national standard in many countries. The article analyzes the purpose, structure, and operation of the latest version of ISO 3951 which was issued in three parts in 2005 and 2006.
Continuous Sampling
Authors: Suresh Kodakanallur Krishnaswamy , von Collani Elart 
Published: September 2014

Abstract
This article considers the control of item‐by‐item production in contrast to the control of lot‐by‐lot production. The methods covered are the continuous sampling plan, CSP‐1, some of its extensions and the corresponding MIL‐STD‐1253C, each of them referring to attribute sampling. The situation, in which these methods can be applied, are for example conveyor and moving assembly lines. The mode of operation and the statistical and operational properties of continuous sampling plans are stated.
Economic Sampling Schemes
Authors: von Collani Elart 
Published: September 2014

Abstract
The process of globalization has resulted in a situation where an increasing number of companies operate production locations all over the world and especially in the so‐called low‐cost countries. This trend has significantly increased the importance of outgoing and incoming quality inspection, normally by sampling inspection, because in many cases the inspection costs represent a considerable part of the total costs. Despite this situation, there is almost no research done on the application of economic sampling plans. The aim of this chapter is to give a short introduction into the state of the art with respect to economic sampling schemes to initiate further research in this important area of statistical quality control.
Sampling Inspection of Products and Statistical Process Control
Authors: Hryniewicz Olgierd 
Published: September 2014

Abstract
Acceptance control charts, originally proposed by Freund, are the tools of statistical process control (SPC) that combine features of classical control charts and sampling plans used in acceptance sampling of products and services. In contrast with classical Shewhart control charts, their design is based on the requirements typical for acceptance sampling plans by variables. In the article, we present the design principles of the acceptance control charts. The similarity between acceptance control charts and acceptance sampling systems shows that the latter might be used for the purpose of SPC.
Sampling in Software Development
Authors: Runeson Per 
Published: September 2014

AbstractIn software development, sampling inspection is applied during the development process, as the production process for software is trivial. Insoftware inspection, which is a manual scrutiny of software code or specifications, sampling is applied to estimate the number of remaining faults and to guide which parts of the artifact to inspect. Capture–recapture techniques are used in the fault content estimation. Insoftware testing, which is the dynamic execution of code, sampling is implicitly or explicitly applied to select test cases. Since the number of states is extremely high in software products, a complete coverage of all cases is practically impossible. In addition to scoping the tests to a reasonable magnitude of size, sampling is used to estimate the number of remaining faults and to predict the operational failure behavior of the software, e.g. through software reliability growth models. The theory is quite well established in the field, while the industrial practice is underdeveloped.
Sampling in Data Mining
Authors: Weiß Christian H. 
Published: September 2014

Abstract
After a brief review of basic terms and concepts of knowledge discovery in databases (KDD) and data mining, this article investigates aspects of sampling in data mining. A general scheme of sampling, and particular techniques used in data mining are discussed. The main objective of this article is to provide an overview on areas of KDD and data mining, where sampling approaches are applied.
Sampling in Implementation of Statistical Process Control
Authors: Nishina Ken 
Published: September 2014

AbstractIn this article, we explain how to obtain data by sampling and implement statistical process control (SPC) over its life cycle. The SPC life cycle consists of three steps: mass production preparation, early‐stage mass production, and routine mass production. In mass production preparation, machine performance is ensured by sampling according to an experimental design. In mass production stage, by sampling in control charts, process variation is reduced and process capability is ensured. Sampling to prevent the escape of a nonconforming product in routine mass production is also explained. In addition, considering semiconductor wafer process, the inherent process variation related to sampling is introduced.
Internal and External Quality Measures
Authors: Ograjenšek Irena 
Published: September 2014

AbstractThere are two distinct general approaches to the use of statistical methods in quality improvement. These are theproduction approachand themarketing approach. The production approach focuses on quality improvement from the management (internal) point of view using the statistical quality control (SQC) tools and producing predominantly internal quality measures. The marketing approach builds on the customer (external) assessment of quality using tools of survey research, producing predominantly external quality measures. One of the biggest challenges that companies face is a meaningful integration of these two groups of measures with the goal of facilitating continuous quality improvement.
k

‐out‐of‐

n

Systems
Authors: Shepherd Deborah K. 
Published: September 2014

Abstract

A
k
‐out‐of‐
n
system is a system with
n
components which functions or works if at least
k
out of the
n
components work. Parallel and series systems are special cases of a
k
‐out‐of‐
n
system. There are many different arrangements of the components in a
k
‐out‐of‐
n
system and many assumptions that can be made about the lifetimes of the components. In this chapter, the lifetimes of the components in the system are assumed to be independent and identically distributed. The reliability function of the system is found by calculating the probability that at least
k
components out of the
n
components work. The mean time to failure of the system is also found for the case of components with exponential distributions of lifetimes. In addition, the reliability function for a linear and circular consecutive
k‐out‐of‐n
system is given.
Modules and Modular Decomposition
Authors: Zuo Ming J. 
Published: September 2014

Abstract
Modules are system structures that have well‐established reliability evaluation algorithms. The technique of modular decomposition identifies such modules in complex systems and uses their efficient reliability evaluation algorithms for reliability evaluation of the whole system. In this article, we provide the formal definitions of modules and modular decomposition for system reliability analysis, illustrate the technique of modular decomposition, and outline reliability evaluation algorithms for several well‐studied modules. References for further reading in this area will also be provided.
Asymptotic Reliability Analysis of Very Large Structural Systems
Authors: Adhikari Sondipon 
Published: September 2014

Abstract

In the reliability analysis of safety‐critical complex engineering structures, a very large number of the system parameters can be considered to be random variables. The difficulty in computing the failure probability using the classical first and second‐order reliability methods (FORM and SORM) increases rapidly with the number of variables or “dimension”. On the basis of the asymptotic distribution of quadratic form in Gaussian random variables, two formulations, for the case when the number of random variables
n
→ ∞, are provided. The first is called
strict asymptotic formulation
and the second is called
weak asymptotic formulation
. Both approximations result in simple closed‐form expressions for the probability of failure of an engineering structure. The proposed asymptotic approximations are compared with existing approximations and Monte Carlo simulations using numerical examples.
Inflation Impact on Aggregate Claims
Authors: Garrido José , Léveillé Ghislain 
Published: September 2014

Abstract
Inflation can have a significant impact on insured losses. Insurance companies recognize this effect on their aggregate claims when setting premiums and reserves.
Interest earned by insurance companies on their reserves can, to some extent, offset the effect of inflation on claims. This article looks at the combined effect of these economical variables, inflation and interest, on the distribution of aggregate claims.
S
undt's Classes of Distributions
Authors: Vernic Raluca 
Published: September 2014

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.
Decrement Analysis
Authors: Pollard John 
Published: September 2014

AbstractDecrement analysis intends to provide approximate values for some actuarial quantities that are not fully observable. The most common and single‐mode decrement is death and mortality rates are not fully known due to censoring of the observed lives. By using graduation methods, workable alternatives are provided. More complicated situations arise in a multiple‐decrement situation where the dependence between different causes needs to be taken into account.
Subexponential Distributions
Authors: Klüppelberg Claudia 
Published: September 2014

AbstractSubexponential distributions are a special class of heavy‐tailed distributions. The name arises from one of their properties that their tails decrease slower than any exponential tail (3). This implies that large values can occur in a sample with nonnegligible probability, which makes the subexponential distributions natural candidates for modeling situations in which some extremely large values occur in a sample compared with the mean size of the data. Such a pattern is often seen in insurance data, for instance, in fire, wind–storm, or flood insurance (collectively known as catastrophe insurance). Subexponential claims can account for large fluctuations in the surplus process of a company, increasing the risk involved in such portfolios.
Censoring
Authors: Papaioannou Takis 
Published: September 2014

Abstract

Censoring is a process or a situation where we have partial information or incomplete observations. It is an area in statistics, engineering, and actuarial science, which has received widespread attention in the last 40 years, having been influenced by the increase in scientific computational power. The purpose of this article is to present the concept, the idea of censoring, to describe various types or kinds of censoring, and to point out its connections with survival analysis and
actuarial methods
. Censoring is closely related with censored data and is a major topic in
biostatistics
and reliability theory (life testing).
Regenerative Processes
Authors: Wolff Ronald W. 
Published: September 2014

Abstract
A regenerative process is a stochastic process with the property that after some (usually) random time, it starts over in the sense that, from the random time on, the process is stochastically equivalent to what it was at the beginning.
In many applied fields such as telecommunications, finance, and actuarial science, important probability models turn out to be regenerative. Properties of regenerative processes and the mathematical methods used in their analysis are fundamental in the analysis and understanding of these models.

For
classical
regenerative processes, cycles and cycle lengths are i.i.d. (independent and identically distributed). This assumption has been weakened over time. Now, processes satisfying the first paragraph above are regarded as regenerative, without any independent assumptions.

This article introduces the classical case in the section ‘Classical Regenerative Processes’, presents their time‐average properties in the section ‘Time‐average Properties, Classical Case’, and briefly treats more advanced topics in the section ‘Existence of Limits; Combining Processes’.
Logistic Regression for Credit Scoring
Authors: Dierckx Goedele 
Published: September 2014

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.
Dynamic Financial Modeling of an Insurance Enterprise
Authors: Hardy Mary R. 
Published: September 2014

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.
Homeowners Insurance
Authors: Fry Martin 
Published: September 2014

Abstract
Homeowners insurance indemnifies owners against property damage arising from a number of insurable events. The insurance covers building costs and repairs, and rental costs if a property becomes uninhabitable. Factors affecting the premium for a homeowners policy are the sum insured, the construction materials of the building, the use of the building, and its location. Deductibles are often imposed.
Lapses
Authors: Fry Martin 
Published: September 2014

Abstract
A lapse is a failure by a policyholder to accept an insurer's offer to renew an expiring policy. Lapses differ from cancellations, which require specific action by a policyholder during the policy term. Insurers monitor lapse rates, as high lapse rates may indicate that an insurer's policies are uncompetitive. In practice, renewal rates are often monitored; they are calculated as (1‐lapse rates).
L
loyd's
Authors: Michaelson Robin 
Published: September 2014

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.
Audit
Authors: Coestier Bénédicte , Fombaron Nathalie 
Published: September 2014

AbstractIn an insurer–policyholder relation, insurees may use their informational advantage about the occurrence and/or magnitude of their losses to exaggerate the claims. Claims auditing may be a means to manage this type of insurance fraud. The economic analysis of audit has been developed in the literature principally using Costly State Verification models. After recalling the more basic results of CSV models, we discuss the major limitation of these auditing procedures, namely, the commitment assumption, and finally consider several solutions to the no‐commitment problem.
Underwriting Cycle
Authors: Klein Robert 
Published: September 2014

Abstract
The property‐casualty insurance industry is notorious for its pattern of rising and falling prices and supply of coverage, commonly known as the “underwriting cycle.” This article explains this phenomenon, reviews various theories about its causes, and discusses proposed government remedies. The “real cycle” in the price and supply of insurance is distinguished from the “accounting cycle” in reported performance ratios. Several hypothesized causes of the real cycle are examined, including adverse loss shocks, movements in interest rates, under‐pricing due to naivety or excessive risk taking, and greater uncertainty about the distribution of expected losses. Government policies that help to control claims costs will be the most effective in promoting greater market stability.