bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stochastic Resonance
Authors: Bulsara Adi R. 
Published: September 2014

AbstractNoise is usually considered a nuisance or a laboratory curiosity, better avoided, in most practical applications. However, certain nonlinear dynamic systems in nature (the most ubiquitous of these being, of course, the central nervous system) appear to utilize the background noise as an aid to the analysis of low‐intensity signals embedded in the noise floor. A phenomenon known as stochastic resonance (SR) has been studied in the context of this amplification of weak signals in a noise background; the effect is only one of a plethora of diversified behavior that is displayed by nonlinear dynamic systems in the presence of noise, and can actually lead to an energy transfer from the noise floor to the signal feature (in the response), under the appropriate operating conditions.
SemiParametric Analysis of Competing Risks Data
Authors: Sundaram Rajeshwari , Balakrishnan N.K. 
Published: September 2014

AbstractCompeting risks data are typically encountered in biomedical/epidemiological studies. Examples of such data can be found in studying labor in women as labor can be either spontaneous or due to medical intervention (example: delivery by cesarean) or due to membrane rupture leading to labor. Typically in competing risks framework, each individual is exposed to K distinct types of risks and the eventual failure can be attributed to precisely one of the risks. As is usual in survival data, these competing risks data are further subjected to censoring. Two quantities of considerable interest include, the cause‐specific hazard and the corresponding cumulative incidence function for a specific cause. In this article, we will review various modeling approaches for assessing the effects of covariates through modeling cause‐specific hazard. We will also discuss various approaches for constructing confidence intervals as well as confidence bands for the cause‐specific cumulative incidence function of subjects with given values of the covariates.
Group Sequential Methods in Biomedical Research
Authors: Liu Aiyi , Liu Chunling , Yu Kai F. 
Published: September 2014

AbstractGroup sequential methods have become a common practice in biomedical research due to various reasons such as ethical, administrative, and economic concerns. In testing statistical hypotheses a group sequential procedure allows the hypotheses to be tested repeatedly at several time points after a group of observations have been completed.The sequential methods pose great challenges to statisticians. These challenges include not only finding appropriate methods of determining critical values at each interim test to control error rates such as false positive/negative rate, but also developing efficient inferential procedures for secondary analysis such as estimation, confidence intervals,p‐values and secondary endpoints. This is because the data‐based random stopping of the sampling process introduces bias to conventional procedures that are appropriate in a non‐sequential (fixed‐size) setting.In the context of Brownian motion as approximation to various test statistics, this article presents an overview of several common group sequential testing procedures, and methods for computing point estimation, confidence intervals, andp‐values.
Statistical Quality of Life
Authors: Mesbah Mounir 
Published: September 2014

AbstractHealth Related Quality of Life (HrQoL) is a latent variable, indirectly observed through individual responses to a set of questions (items) forming the HrQoL questionnaire. The first part of the paper is about its measurement which remains a complex issue. This measurement process is done via instruments (questionnaires) which are validated through the intermediary of some fundamental models, mainly based on unidimensionality principle and some specific conditional independence properties. We explain how these measurement models are statistically validated, and how measurements they provide must be statistically analyzed in studies involving other external variables. Finally, a few important recent issues in the field are presented.
Statistical Modeling of Human Fecundity
Authors: Sundaram Rajeshwari , Buck Louis Germaine M. , Kim Sungduk 
Published: September 2014

AbstractHuman fecundity is of great interest to reproductive scientists, as well as to couples trying to conceive. It is measured by the probability of pregnancy in a menstrual cycle for a sexually active couple who is not contracepting. Reproductive epidemiologists are interested in models for human fecundity to identify the determinants and to identify preventable causes of reproductive dysfunction. There is also suspicion that various environmental factors negatively affect the human fertility. Motivated by these issues, there has been considerable interest in developing biologically precise statistical models for human fecundity. This chapter begins with a brief discussion of various biological and behavioral factors that potentially impact pregnancy. Furthermore, emphasis is made on various biological and behavioral factors that impact pregnancy but are not measurable. Next, we review various statistical models for pregnancy that are available in literature, with emphasis on models that are biologically relevant. We also indicate some outstanding issues that need to be addressed to build stronger statistical models that are biologically valid. We conclude with an analysis of the New York Anglers Cohort Prospective Pregnancy study.
Estimation of Travel Distance
Authors: Brimberg Jack , Walker John H. 
Published: September 2014

AbstractIn this chapter we discuss the use of empirical distance functions in business models. The traditional role of distance functions has been to estimate travel distances or times in a given transportation system. We examine the properties of distance functions that make them useful in this role, and review popular ones such as the Euclidean norm, rectangular (or Manhattan) norm, and the more general weighted ℓpnorm and block norm. Statistical methods used to estimate the parameters of an empirical distance function from actual travel distance data are presented. This includes a review of the various goodness‐of‐fit criteria appearing in the literature, and a discussion of their error term distributions. The usefulness of empirical distance functions in modeling service systems of ever‐increasing size and complexity, and their growing relevance in operational and strategic decision processes, are also investigated. Future areas of research are identified in closing.
Mining Functional Data in Prediction Markets
Authors: Foutz Natasha Zhang , Jank Wolfgang 
Published: September 2014

AbstractPrerelease demand forecasting is one of the most crucial yet difficult tasks facing marketers in the $60 billion motion picture industry. We propose functional data analysis of prediction markets to address this long‐standing challenge. In prediction markets, prices of a movie's stock reflect the dynamic demand expectations prior to the movie's release. Using functional data analysis, we identify a small number of distinguishing shapes, e.g., the last‐moment velocity spurt, that carry information about a movie's future demand and produce early and accurate prerelease forecasts.
Directional Statistics: Introduction
Authors: Jammalamadaka S. Rao 
Published: September 2014

Abstract
In many natural and physical sciences the measurements are directions—either in two‐ or three‐dimensions. This chapter briefly introduces this novel area of statistics, and provides a good starting point for further exploration. A basic parametric model called the Fisher‐von Mises distribution is introduced and nonparametric methods such as goodness of fit tests are discussed. Further references are given for exploring related topics such as correlation and regression.
Optimal Sample Size Allocation for Accelerated Degradation Test Based on Wiener Process
Authors: Tseng Sheng‐Tsaing , Tsai Chih‐Chun , Balakrishnan N. 
Published: September 2014

AbstractDegradation tests are widely used to assess the reliability of highly reliable products which are not likely to fail under traditional life tests or accelerated life tests (ALT). However, for some highly reliable products, the degradation may be very slow, and thus it seems impossible to have a precise assessment within a reasonable test time. In such cases, an alternative technique is to use higher stresses to extrapolate the product's reliability at the normal use stress. This is called an accelerated degradation test (ADT). In this article, motivated by a LEDs data, we discuss the optimal allocation problem under accelerated degradation experiment when a Wiener process is used to describe the product's degradation path. We derive the Fisher information and the approximate variance of the estimated mean‐time‐to‐failure (MTTF) under normal use. Three optimality criteria are defined and the optimal allocation of test units are determined. Finally, the LEDs data is illustrated to demonstrate the efficiency of the optimal allocation of test units.
Statistical Models for Damage Accumulation
Authors: Castillo Enrique , Fernández‐Canteli Alfonso 
Published: September 2014

Abstract
As an initiation to the damage accumulation problem, different damage measures are presented, including a discussion of the properties that should be expected from these measures when not only mean values but uncertainties are required. Next, the role played by the Buckingham theorem and similarity and self‐similarity conditions in the process of building models is justified. Special attention is devoted to dynamic system based models, the assumptions of which are described and discussed together with the corresponding implications on the resulting models. Since building models based on their expected properties more than on artificial assumptions are defended, the technique of functional equations, a very innovative and powerful mathematical tool, is used to represent these properties and to obtain the resulting models. An illustration is given to derive the S‐N models that allow the effect of stress level and range on the fatigue life to be considered and the associated uncertainty to be defined. Similar procedures are applied to the derivation of crack growth and crack growth rate models. Due to the practical importance of the load history, the rainflow counting technique is described using the stress level to define the rainflow matrix. Finally, an example of application and final remarks are provided.
Bayesian Networks
Authors: Castillo Enrique , Hadi Ali S. 
Published: September 2014

AbstractMany practical applications require the specification of the joint probability density (JPD) of a large set of random variables. This task can be very difficult in practice. Bayesian and Markov networks are two graphically based models that make the specification of the JPD and the propagation of uncertainty simple and efficient because they exploit the conditional independence structure of the JPD. This entry deals with Bayesian networks, also known as Belief networks. Markov networks are discussed in another entry. Bayesian networks are defined and their usefulness is illustrated by examples of the two most commonly used Bayesian networks, namely, the multinomial and Gaussian networks. The questions about how Bayesian networks can be constructed, how Bayesian networks can be learnt from data, and how uncertainty can be propagated when new evidence becomes available are also discussed.
Epidemiological Statistics ‐ Further Issues
Authors: McNeil Don 
Published: September 2014

AbstractEpidemiological studies use statistical methods to understand the factors that cause, reduce, and prevent diseases in human populations. These studies estimate the parameters in a target population based on representative samples, and can be qualitative or quantitative, descriptive or comparative, and observational or experimental. Parameters of interest include levels of disease and associations between risk factors and disease outcomes. Concept maps are useful for showing causal paths between risk factors, disease outcomes, and intervening variables (confounders) that could distort the association. The statistical methods can be classified by the type and role of the data, and are subject to both inaccuracy or lack of statistical power (due to sampling variability) and bias (due to faulty measurement, differential selection of subjects, or confounding). Matching of subjects can improve accuracy. Mantel–Haenszel methods can reduce confounding bias. Meta‐analysis is used to combine results from different studies. Statistical models include linear, logistic, and Poisson regression, and the proportional hazards model for handling censored data.
Spacings, Linear Combinations of
Authors: Huffer Fred W. , Lin Chien‐Tai 
Published: September 2014

AbstractProblems involving the distribution of linear combinations of spacings occur in many areas of statistics. This article gives expressions for the density and distribution function of an arbitrary linear combination of spacings. General methods are also presented for computing the distribution of the maximum or minimum of any finite collection of linear combinations of spacings. When the coefficients of the linear combinations are rational, these methods can be implemented on computer algebra systems to produce exact symbolic expressions for the distributions. These expressions can be used to compute numerical answers accurate to any required degree of precision.
Stirling Numbers and Generalizations
Authors: Charalambides Ch. A. 
Published: September 2014

Abstract

The Stirling numbers of the first and second kind enumerate the permutations of a finite set that are decomposed into a given number of cycles and the partitions of a finite set into a given number of subsets, respectively. Basic properties of these numbers and their generalizations of probabilistic and statistical interest are reviewed. Specifically, explicit expressions, generating functions, recurrence relations, and other useful properties of these numbers are presented. Further, the Lah numbers and the generalized factorial coefficients, closely related to the Stirling numbers, are discussed. The noncentral and the associated Stirling and related numbers are briefly presented. The generalized Stirling and Lah numbers and, in particular, the
q
‐Stirling and
q
‐Lah numbers are only quoted. Probabilistic and statistical applications of the Stirling and related numbers in expressing the probability mass function of certain (a) urn‐model distributions, (b) occupancy distributions, (c) convolutions of truncated discrete distributions and (d) compound discrete distributions are exemplified.
Concave and Log‐Concave Distributions
Authors: Sengupta Debasis 
Published: September 2014

Abstract
This article deals with the context, scope, properties, applications and inference of concave and log‐concave distribution functions. These two classes of distributions are compared and contrasted with other related classes. It is shown that such distributions can arise from a variety of situations. Characterizations as well as closure and other properties of these classes are reviewed. Discrete versions of these classes and relation with their continuous counterparts are discussed. Implications of log‐concavity of a distribution function in various fields are mentioned. Sharp inequalities for the concave and log‐concave distribution functions having support on the positive real line are provided. Literature on estimation of concave distribution functions is briefly reviewed.
Binomial Distribution: Sample Size Estimation
Authors: Grevstad Nels 
Published: September 2014

AbstractThe need to estimate the sample size parameternin the binomial distribution arises in many practical problems, such as estimating wildlife population sizes based on sample counts. Attempts to find reasonable estimators can be traced back to the early 1900s, when the method of moments estimator was proposed. But the method of moments estimator, as well as the maximum likelihood estimator, suffers serious shortcomings in terms of performance, not the least of which is a highly unstable behavior. Following a few examples and a brief history of the problem, this entry details some of the difficulties that arise when estimatingn, and presents the results of attempts that have been made to overcome those difficulties either by modifying the method of moments or maximum likelihood estimator, or adopting a Bayesian approach.
U

‐ and

V

‐Statistics
Authors: Shieh Grace S. 
Published: September 2014

Abstract

This article reviews the development of
U
‐ and
V
‐statistics from mid‐1990s to 2001. Three categories of new results are reviewed: (1) asymptotic behavior of
U
‐ and
U
‐related statistics, (2) asymptotic distribution theory (including asymptotic distribution, Berry‐Esseen theorem, and Edgeworth expansion), and (3) a new subclass,
U
‐statistics with a kernel of indeterminate degeneracy.
Hazard Regression
Authors: Kooperberg Charles , Stone Charles J. , Truong Young K. 
Published: September 2014

AbstractHazard regression (HARE) is a regression methodology using linear splines to model the conditional log‐hazard function based on possibly censored survival data with one or more covariates. The methodology is inspired by the adaptive multiple regression methodologyMARS.HAREdoes not assume the proportionality used in the Cox model, but it contains proportional models as a special case. It provides great flexibility in modeling covariate effects and the hazard function using polynomial splines and stepwise addition and deletion of basis functions. Copyright © 2015 John Wiley & Sons, Ltd.
Markov Networks
Authors: Castillo Enrique , Hadi Ali S. 
Published: September 2014

AbstractMany practical applications require the specification of the joint probability density (JPD) of a large set of random variables. This task can be very difficult in practice. Bayesian and Markov networks are two graphically based models that make the specification of the JPD and the propagation of uncertainty simple and efficient because they exploit the conditional independence structure of the JPD. This entry deals with Markov networks. Bayesian networks are discussed in another entry. Markov networks are defined and the questions about how Markov networks can be constructed, how Markov networks can be learnt from data, and how uncertainty can be propagated when new evidence becomes available are also discussed.
Graph‐Theoretical Methods
Authors: Quiroz Adolfo J. 
Published: September 2014

AbstractThe use of graph‐theoretical structures, principally the Minimal Spanning Tree and thekNearest Neighbors Graph, allows the multivariate generalization of certain nonparametric statistical procedures and suggests the consideration of new ones. The areas of statistical application of graph theoretic procedures include the two‐sample problem, clustering, outlier identification, hypothesis testing, and dimension identification.
Statistical Methods in Inventory Effect and Analysis
Authors: Lien Donald 
Published: September 2014

AbstractInventory effect suggests that the correlation between spot and futures prices is positively related to the inventory level. It was proposed to be a possible explanation for backwardation equilibrium in futures markets. A test for the inventory effect requires the specification of the joint distribution between spot and futures prices. This chapter summarizes four approaches currently available in the literature: bivariate normal distributions, bivariate log‐normal distributions, ordered bivariate normal distributions, and ordered bivariate log‐normal distributions. These approaches generate different conclusions, indicating the result is not robust to the distribution assumption. As a consequence, the inventory effect may prevail in some commodity markets but not in others.
Partial Least Squares Regression
Authors: Helland Inge 
Published: September 2014

AbstractPartial least squares regression was introduced as an algorithm in the early 1980s, and it has gained much popularity in chemometrics. PLSR—or PLSR1—is a regression method for collinear data, and can be seen as a competitor to principal component regression. The method makes it possible to combine prediction with a study of a joint latent structure in thex,y‐variables. PLSR2 is a generalization to several dependent variables. PLSR1 can be understood from a statistical point of view via its parameter algorithm and the corresponding population model.
Geographic Information Systems
Authors: Griffith Daniel A. 
Published: September 2014

AbstractA geographic information system (GIS) is a computer hardware and software information system designed to capture, edit, manage, house, manipulate, analyze, and display georeferenced data. It comprises high‐resolution scientific visualization capabilities, large‐capacity electronic storage devices, efficient and effective structures for data storage and retrieval, high‐volume communication channels, specialized algorithms for data integration and reliability analysis, and specialized query languages. Its reference point is a digital map. The first GIS was developed by the Canadian government and implemented in 1964. A differentiating feature among GISs is whether the underlying map involves a raster or vector surface partitioning. Spatial statistics requires attribute data, a map, and the tagging of each data observation to a location on a map, items furnished by a GIS. A GIS also supplies a practical and useful way to reveal spatial and temporal relationships among data. The UCGIS is an organization whose mission is to serve as an effective unified voice for the community of GIS users.
Geostatistics
Authors: Wackernagel Hans 
Published: September 2014

AbstractGeostatistics is an application of the theory of random functions to spatially distributed data. The methods, initially proposed by Matheron in mining and petroleum exploration, found their way back to mainstream statistics more than a decade ago. The geostatistical methodology can be subdivided into linear and multivariate geostatistics, nonstationary geostatistics, nonlinear geostatistics, geostatistical simulation.
Financial Time Series
Authors: Tsay Ruey S. 
Published: September 2014

AbstractFinancial time series analysis is concerned with theory and practice of asset valuation over time. This article introduces key features and properties of financial time series and discusses econometric and statistical methods available for analyzing such series. It also discusses recent developments in financial time series analysis in the advent of high‐frequency transaction data and in estimation of stochastic diffusion equations for derivative pricing.
Nonlife Loss Reserving
Authors: Alba Enrique , Hernández Diego 
Published: September 2014

Abstract
This article presents a brief description of nonlife claims reserving methods. Nonlife claims reserves are financial resources, set aside from all other insurance operations, as a provision for claims not completely settled at a specific time. Since these provisions constitute the largest liabilities carried by nonlife insurers, their determination affects many aspects of the business, such as ratemaking and the assessment of the insurer's net worth. From the actuarial point of view, reserving is a problem of estimation, or more precisely, forecasting. As a consequence, reserving methods range from very simple deterministic techniques to some very sophisticated ones, based on stochastic models for claim settlement. We present, in a structured way, the main approaches in the field, and provide the interested reader with references for further study.