bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analytical Methods for Risk Management: An Engineering Systems Perspective
Authors: R. Garvey Paul 
Published: September 2014

AbstractRisk is a driving consideration in decisions that determine how engineering systems are developed, produced, and sustained. Critical to these decisions is an understanding of risk and how it affects the engineering of systems. The process of identifying, measuring, and managing risk is known as risk management. Applied early, risk management can expose potentially crippling areas of risk in the engineering of systems. This provides management time to define and implement corrective strategies. Moreover, risk management can bring realism to technical and managerial decisions that define a system's overall engineering strategy. This paper presents an introduction to processes and analytical practices in the management of risk, as it arises in the engineering of systems. These practices have evolved from a variety of systems engineering projects and initiatives. Traditional engineering systems and risk analysis approaches are discussed.
Methods of Risks Estimation and Analysis of Business Processes
Authors: Kijima Masaaki , Tanaka Keiichi 
Published: September 2014

Abstract
This chapter provides various alternatives to the Black–Scholes model for the pricing of derivative securities. The purpose of such extensions is to make pricing models more realistic in order to incorporate the so‐called smile effect and volatility clustering. Stochastic volatility and interest‐rate models as well as models with Levy processes are explained in detail.
Methods of Risks Estimation and Analysis of Business Processes
Authors: Balakrishnan N. , Naumov A. , Morgunov D. 
Published: September 2014

AbstractIn the paper the model of business processes of a data‐flow type and operations above them are entered into reviewing. The methods of the analysis of business processes on effectiveness are offered. The methods of an risk estimation and analysis of business processes are considered also. The task of risk estimation and using it on practice in managing economic systems with using processing approach is investigated. Conditional‐internal and conditional‐external risks are considered and proved their properties. Metrics of risks estimation are given as highest comparative and absolute losses, average losses etc. Inefficiency of using risk estimation procedures by dispersion and quantiles is shown. Approaches to the business‐processes indicators optimization proposed and investigated in the area “risk‐profit” (“risk‐indicator”). This optimization is realized by using utility function as multicriterion problem. Functional, stochastic dependences between risks are shown, that do not allow to optimize risks only (separate from indicators). The task of risk managing in economic systems is work out in complex with the tasks of it's analysis, modeling and optimization. The problem of factor analysis in deterministic form is investigated too.
Models for Bid Arrivals and Bidder Arrivals in Online Auctions
Authors: Russo Ralph P. , Shyamalkumar Nariankadu D. , Shmueli Galit , Jank Wolfgang 
Published: September 2014

AbstractThe arrival process of bidders and bids in online auctions is important for studying and modeling supply and demand in the online marketplace. Whereas bid arrivals are observable in online auction data, bidder behavior is typically not. A popular assumption in the online auction literature is that a homogeneous Poisson bidder arrival process is a reasonable approximation. This approximation underlies theoretical derivations, statistical models and simulations used in field studies. However, empirical research has shown that the process of bid arrivals is far from homogeneous, as it features early and last‐moment bidding, as well as a self‐similar structure. In this chapter we discuss two types of models: Descriptive models for bid arrivals that were derived based on features of real bid data in online auctions, and models for bidder arrivals that lead to the bid arrival process. The model for the bid arrival process, calledthe BARISTA process, can generate different intensities at different stages of the auctions. We discuss its properties, show how to simulate bid arrivals from it, and how to estimate its parameters. We then describe two bidder behavior models that lead to versions of the BARISTA process. Model adequacy and performance are illustrated via simulation and real data from eBay.
Advances in Robust Parameter Design: From Taguchi's Inner‐Outer Arrays to Combined Arrays
Authors: Evangelaras Haralambos , Koukouvinos Christos , Koutras Markos V. 
Published: September 2014

Abstract
Robust parameter design, originally proposed by Taguchi, is an off‐line technique for maintaining variance reduction and improving quality. According to Taguchi, product variation from the desirable target is affected by two types of factors: control factors and noise factors. The basic idea in robust parameter design is to identify the levels of the control factors so that the product's quality characteristic becomes insensitive to changes in the levels of the noise factors. As a consequence, the effect of uncontrollable variations on the response will be reduced.
Taguchi suggested the use of inner‐outer arrays with the control factors being assigned to an inner array and the noise factors assigned to an outer array. After the collection of the data, a performance measure called signal‐to‐noise ratio is calculated and analyzed in order to identify the optimal levels of the control factors. After the introduction of Taguchi's philosophy in the field of experimental design and data analysis, several new designs and techniques have been explored and adopted in this field. This chapter overviews well established approaches and analysis techniques that have been exploited to the robust parameter design field.
Applicability of Statistics and Probability Theory to Nucleate Pool Boiling Heat Transfer
Authors: Judd R.L. , Balakrishnan N. 
Published: September 2014

AbstractMuch has been learned about nucleate pool boiling over the last sixty years through experimental investigation and recently, it has become possible to predict the relationships governing the nucleation, growth and departure or collapse of bubbles computationally. However, it has not been possible to develop a theory capable of predicting the rate of heat transfer because of the inability to incorporate surface conditions in a comprehensive boiling heat transfer model. A method is proposed to simulate the surface conditions through the interactions of a large population of potentially active nucleation sites and a much smaller population of dominant nucleation sites using Monte Carlo simulation.
Degradation Models and Analyses
Authors: Meeker William , Hong Yili , Escobar Luis 
Published: September 2014

Abstract
For products/systems requiring high reliability or availability, reliability assessment and maintenance actions are often needed in a timely manner. For these systems/products, information on system state and performance can be collected over the system's operating time, providing what is known as degradation data. With appropriate models and data analysis techniques, degradation data are converted to system reliability information and this information is useful for short and long‐term reliability assessment and for planning maintenance actions. This article provides a review of degradation data models and methods that have been used successfully in this area. Commonly used methods for analyzing degradation data, general path models and stochastic models, are described in detail. Models and methods for analyzing data from accelerated degradation tests and accelerated destructive degradation tests are also introduced. Dynamic condition monitoring based on degradation data is covered. Examples are used to illustrate the models and methods covered in this article. The conclusion provides a discussion of test planning, software implementation and the future of degradation data analysis.
Pricing Foreign Exchange Options with Stochastic Volatility
Authors: Ahlip Rehez 
Published: September 2014

AbstractAn exhaustive survey of research on pricing foreign exchange options with stochastic volatility and stochastic interest rates is presented in this article. The article also includes contributions to pricing of currency options with fixed volatility and deterministic interest rates, domestic and foreign, and in a systematic way extend to results obtained for cases which include stochastic volatility and stochastic interest rates. Since the literature in pricing foreign exchange currency option is quite extensive, the article considers some of the more important contributions.
Step‐Stress Testing with Multiple Samples: The Exponential Case
Authors: Balakrishnan N. , Kamps Udo 
Published: September 2014

AbstractStep‐stress models are descriptions of special experiments in the field of accelerated life testing, where the test units are exposed to stress levels that change at intermediate time points during the experiment. The goal is to develop inference for the mean lifetime at each stress level. The time points of stress level change can either be fixed or random. Furthermore, the experiment can be terminated when a certain number of failures is reached or at a pre‐specified time point. These alternative assumptions of the type of the experiment lead to alternative models. Usually the step‐stress models are based on a single experiment. We develop inference for step stress models designed for multiple samples. The stress levels are the same applied to all samples but the duration of exposure under each stress level can vary among the experiments.The likelihood inference is then discussed in detail for the exponential case and different simple step‐stress experiments.
Group‐Randomized Trials, Planning and Analysis of
Authors: Murray David M. 
Published: September 2014

Abstract
Planning a group‐randomized trial is a complex process. Care must be taken to delineate the research question, to create the research team, to plan the research design, to avoid potential design and analytic problems, to select variables of interest and their measures, to plan the intervention, and to ensure adequate power. Failure in any of these areas may lead to a dissappointing result; attention to each will increase the likelihood of success
Categorical Data, Marginal Models for
Authors: Molenberghs Geert 
Published: September 2014

AbstractWe briefly review two building blocks (generalized linear models and linear mixed models) for models for repeated categorical data. Three families of models for repeated categorical data are introduced: marginal models, conditional models, and random‐effects models. A number of marginal models are presented for binary and for ordinal data in turn. Major differences between marginal models and conditional models (such as loglinear models) are discussed.
Folded Transformations
Authors: Hoaglin David C. 
Published: September 2014

Abstract

Introduced in John W. Tukey's book
Exploratory Data Analysis
,
folded transformation
refers to a transformation
T
of a proportion
u
for which
T
(1 −
u
) = −
T
(
u
). Examples include the folded fraction or plurality,
T
(
u
) = 2
u
− 1, and the folded logarithm or logit, logit(
u
) = log

e

(
u
) − log

e

(1 −
u
).
Functional Data Analysis ‐ Theory
Authors: Ramsay J. O. 
Published: September 2014

Abstract
When either data or the models for them involve functions, and when only weak assumptions about these functions such as smoothness are permitted, familiar statistical methods must be modified and new approaches developed in order to take advantage of this smoothness. The first part of the article considers some general issues such as characteristics of functional data, uses of derivatives in functional modelling, estimation of phase variation by the alignment or registration of curve features, the nature of error, and so forth. The second section describes functional versions of traditional methods such principal components analysis and linear modelling, and also mentions purely functional approaches that involve working with and estimating differential equations in the functional data analysis process.
C
opula Process,
A
rchimedean
Authors: Malov S. V. 
Published: September 2014

Abstract

Archimedean copulas have a wide field of applications in probability theory and in mathematical statistics. The copula arose first in the work of Sklar [14]. It is the function which characterizes principal connection of components in a random vector independently of marginal distributions. Archimedean copula process was introduced as a generalization of the scheme of independent random variables. The most of interesting applications of this object was in the order statistics theory and in the extreme values theory. The extreme type theorems and some characterizations of classes of sequences via properties of order statistics and record values can be improved from the class of independent random variables to the class of Archimedean copula processes. In this way the proportional Archimedean copula process appeared as the generalization of the
F
α
‐scheme for independent random variables. Some representations of the Archimedean copula process via independent random variables will be also given.
Elliptically Contoured Distributions
Authors: Fang Kai‐Tai 
Published: September 2014

AbstractThe elliptically contoured distribution (ECD for short) is a class of multivariate distributions that are considered as an extension of the multivariate normal distributions. In this entry, we introduce a historical perspective, basic properties, and subclasses of the ECD. The estimation of parameters of an ECD implies the study of elliptical matrix distributions. A brief review on elliptical matrix distributions is given. Some applications of ECD are discussed.
Income Distribution Models
Authors: Dagum Camilo 
Published: September 2014

Abstract
Since Pareto specified his Type I model in 1895, scores of income distribution (ID) models have been proposed. Many of them follow Edgeworth's approach, starting with a probability function and a transformation of its random variable, to derive a positive asymmetric distribution as an ID model. Some of them are supported by a realistic scientific foundation, others are the outcome of a formal analogy, and the remainder are only ad hoc specifications. Almost all of these models are organized here in three generating systems, and their respective foundations are outlined. We introduce a set of seven basic properties to be fulfilled by a probability distribution for consideration as an ID model. Their main purpose is to guide researchers in the evaluation and choice of an appropriate and robust model to describe observed IDs in different epochs and countries in different states of socioeconomic development, and according to several socioeconomic attributes of the observed populations of economic units.
Dependence, Tests for
Authors: Hollander Myles 
Published: September 2014

AbstractThis paper is designed to provide a sound introduction for a reasonably well informed reader who is, however, not a specialist in tests for dependence. The paper contains references to many tests but emphasizes the parametric test of independence based on Pearson's sample correlation coefficient*rand certain nonparametric tests based on ranks. The ranks tests are generally preferable to the test based onrin that they have wider applicability, are much less sensitive to outlying observations, are exact under mild assumptions that do not require an underlying bivariate normal* population, and have good efficiency (power) properties.
Gamma‐Minimax Inference
Authors: Ruggeri Fabrizio 
Published: September 2014

AbstractΓ‐minimax theory deals with the problem of selecting decision rules under partial prior information on the distribution of the unknown parameter. The selected rule is the one minimizing the supremum of the payoff (Bayes and regret risk in a frequentist approach, and posterior expected loss and regret in a Bayesian one) over the priors in Γ.
Counting Process: Basic
Authors: Keiding Neils 
Published: September 2014

AbstractSince the pioneering work of O.O. Aalen (1975, 1978), the statistical theory of continuous‐time survival analysis and event history analysis has been based on the probabilistic theory of counting processes, martingales, and stochastic integrals. The important features are a full mathematical theory of censoring, the most important feature of these areas of applied statistics, and a master central limit theorem for martingales that allows a general asymptotic theory of many functionals used for parametric and nonparametric estimation and hypothesis testing. In this way, the tool of counting processes has provided a uniform background for what used to be scattered results.While survival analysis is the basic example of these techniques, more detailed statistical models are also covered, including competing risk models and multistate processes including illness‐death models.
Step‐Stress Testing
Authors: Gouno Evans 
Published: September 2014

Abstract
Step‐stress accelerated life test have been studied at least since the 1950s. The strategy consists in testing items successively under different levels of stresses. The main advantage of this strategy is to overcome the issue of selecting the proper level of stress that generates enough failures to make inference in classical accelerated life test where the stress is constant. It reduces test time and the variability of the failure times. Various types of data are obtained from step‐stress tests. According the nature of the basic assumptions, different statistical techniques can be used. The following work gives an overview of various approaches to handle step‐stress accelerated life test data.
Hazard Change Point Estimation
Authors: Gürler Ülkü 
Published: September 2014

Abstract
In survival and reliability studies, hazard‐rate function plays an important role. Sudden changes in the hazard function may occur due to treatment effects or maintenance activities. A problem of statistical interest is the estimation of the times of the changes, as well as their sizes. In this note, we review the basic estimation methods for hazard change‐point models for complete data. Some related literature is also provided for estimation with incomplete data and hypothesis testing.
Principal Differential Analysis
Authors: Ramsay James O. 
Published: September 2014

AbstractDifferential equation systems are often a natural way of describing sets of variables changing over time or space. For example, in controlling industrial systems, engineers consider the time course of a set of inputs and outputs for a system such as a chemical reactor with a view to developing useful feedback processes. Principal differential analysis uses functional data to estimate a system of linear differential equations that fit the data. The estimate minimizes an integrated squared error criterion. The coefficient functions that define the linear differential equation are estimated by expansions in terms of basis functions such as Fourier series orB‐splines, and the estimates are calculated rapidly and without iteration.
Functional Networks
Authors: Castillo Enrique , Hadi Ali S. 
Published: September 2014

AbstractFunctional networks (FNs) are a general framework useful for solving a wide range of problems in various fields. In FNs, the network structure is not necessarily a black box; it is derived from the reality being modeled, that is, it is design‐driven. Unlike in neural networks (NNs), the neural functions in FNs are true multiargument, and different for all neurons. In FNs, neural functions are learned instead of weights as in NNs. In addition, neural functions can be forced to output the same values, thus leading to a system of functional equations that impose strong conditions on the neuron functions and allow simplification of the topology. FNs are illustrated by an example of application.
Correlation, Weighted
Authors: Shieh Grace S. 
Published: September 2014

Abstract
Testing the equality of two population correlation coefficients when the data are bivariate normal and Pearson correlation coefficients are used as estimates of the population parameters is a straightforward procedure covered in many introductory statistics courses. The coefficients are converted using Fisher's z‐transformation with standard errors (N‐3)‐1/2. The two transformed values are then compared using a standard normal procedure. When data are not bivariate normal, Spearman's correlation coefficient rho is often used as the index of correlation. Comparison of two Spearman rhos is not as well documented. Three approaches were investigated using Monte Carlo simulations. Treating the Spearman coefficients as though they were Pearson coefficients and using the standard Fisher's z‐transformation and subsequent comparison was more robust with respect to Type I error than either ignoring the nonnormality and computing Pearson coefficients or converting the Spearman coefficients to Pearson equivalents prior to transformation.
Fisher Information: Nonstandard Situations
Authors: Zheng Gang , Balakrishnan N. 
Published: September 2014

AbstractFisher's measure of information is a fundamental concept in statistical science. It plays an important role in statistical theory of inference. The definition and applications of Fisher information in standard inference based on random samples have been discussed extensively in the literature. The Fisher information in nonstandard situations involving ordered data has recently attracted considerable attention with discussions focusing on Fisher information in order statistics, percentiles, randomly censored data, concomitants, progressively censored data, and record values, with numerous applications including optimal linear estimates, characterizations, ranked set sampling, and genetic studies. Direct calculation of Fisher information in these nonstandard situations is tedious and computationally difficult. Main techniques to simplify the calculation are discussed here with many examples. Finally, some applications of Fisher information in ordered data are also highlighted.
Statistical Methods in Risk Management by Futures Clearinghouses
Authors: Shanker Latha 
Published: September 2014

Abstract
Futures clearinghouses guarantee the open futures positions of their clearing members. The risk that clearinghouses face as a consequence is managed by the following five tools‐margin requirements, daily settlement, capital requirements, daily price limits and position limits. Clearing members deposit margin with the clearinghouse to support their open futures positions. Under the system of daily settlement of profits and losses, the clearinghouse transfers funds from the margin accounts of clearing members who have incurred losses to the accounts of members who have incurred profits. The margin requirement is usually set to cover the loss expected in a normal trading day. Losses of clearing members in excess of their margin are supported by requiring each member to contribute to the capital requirements of the clearinghouse. The daily price limit specifies the daily trading range for the futures price. A properly chosen price limit can reduce the margin and capital requirement below the levels required in its absence. The position limit constrains the net speculative futures position of one individual to reduce the possibility of clearinghouse failure due to default by one speculator. The optimal values for the tools of risk management depend on the statistical distribution of futures price changes. Moreover, the tools have to be carefully chosen so as to protect the clearinghouse without driving traders away from the market. Evaluation of the different systems for setting the optimal values of these tools and their effects upon the volatility and trading activity in futures markets also depends on the application of statistical tests. This article therefore reviews the statistical methodology used by previous research on risk management by futures clearinghouses.