bluuebunny/crossref_metadata_2025_split huggingface dataset:
Road trafficking description and short term travel time forecasting, with a classification method
Authors: Loubesi Jean‐Michel , Maza Élie , Lavielle Marc , Rodriguez Luis 
Published: February 2009

AbstractThe purpose of this work is, on the one hand, to study how to forecast road trafficking on highway networks and, on the other hand, to describe future traffic events. Here, road trafficking is measured by vehicle velocities. The authors propose two methodologies. The first is based on an empirical classification method, and the second on a probability mixture model. They use an SAEM‐type algorithm (a stochastic approximation of the EM algorithm) to select the densities of the mixture model. Then, they test the validity of their methodologies by forecasting short term travel times.
Some robust design strategies for percentile estimation in binary response models
Authors: Biedermann Stefanie , Dette Holger , Pepelyshev Andrey 
Published: February 2009

AbstractFor the problem of percentile estimation of a quantal response curve, the authors determine multiobjective designs which are robust with respect to misspecifications of the model assumptions. They propose a maximin approach based on efficiencies which leads to designs that are simultaneously efficient with respect to various choices of link functions and parameter regions. Furthermore, the authors deal with the problems of designing model and percentile robust experiments. They give various examples of such designs, which are calculated numerically.
A robust prediction error criterion for pareto modelling of upper tails
Authors: Dupuis Debbie J. , Victoria‐Feser Maria‐Pia 
Published: February 2009

AbstractEstimation of the Pareto tail index from extreme order statistics is an important problem in many settings. The upper tail of the distribution, where data are sparse, is typically fitted with a model, such as the Pareto model, from which quantities such as probabilities associated with extreme events are deduced. The success of this procedure relies heavily not only on the choice of the estimator for the Pareto tail index but also on the procedure used to determine the number k of extreme order statistics that are used for the estimation. The authors develop a robust prediction error criterion for choosing k and estimating the Pareto index. A Monte Carlo study shows the good performance of the new estimator and the analysis of real data sets illustrates that a robust procedure for selection, and not just for estimation, is needed.
Modelling map positional error to infer true feature location
Authors: Barber Jarrett J. , Gelfand Alan E. , Silander John A. 
Published: February 2009

AbstractThe authors consider the issue of map positional error, or the difference between location as represented in a spatial database (i.e., a map) and the corresponding unobservable true location. They propose a fully model‐based approach that incorporates aspects of the map registration process commonly performed by users of geographic informations systems, including rubber‐sheeting. They explain how estimates of positional error can be obtained, hence estimates of true location. They show that with multiple maps of varying accuracy along with ground truthing data, suitable model averaging offers a strategy for using all of the maps to learn about true location.
Testing multivariate uniformity: The distance‐to‐boundary method
Authors: Berrendero José R. , Cuevas Antonio , Vjosázquez‐grande Francisco 
Published: February 2009

AbstractGiven a random sample taken on a compact domain S ⊂ d, the authors propose a new method for testing the hypothesis of uniformity of the underlying distribution. The test statistic is based on the distance of every observation to the boundary of S. The proposed test has a number of interesting properties. In particular, it is feasible and particularly suitable for high dimensional data; it is distribution free for a wide range of choices of 5; it can be adapted to the case that the support of S is unknown; and it also allows for one‐sided versions. Moreover, the results suggest that, in some cases, this procedure does not suffer from the well‐known curse of dimensionality. The authors study the properties of this test from both a theoretical and practical point of view. In particular, an extensive Monte Carlo simulation study allows them to compare their methods with some alternative procedures. They conclude that the proposed test provides quite a satisfactory balance between power, computational simplicity, and adaptability to different dimensions and supports.
Nonparametric estimation of copula functions for dependence modelling
Authors: Chen Song Xi , Huang Tzee‐Ming 
Published: February 2009

AbstractCopulas characterize the dependence among components of random vectors. Unlike marginal and joint distributions, which are directly observable, the copula of a random vector is a hidden dependence structure that links the joint distribution with its margins. Choosing a parametric copula model is thus a nontrivial task but it can be facilitated by relying on a nonparametric estimator. Here the authors propose a kernel estimator of the copula that is mean square consistent everywhere on the support. They determine the bias and variance of this estimator. They also study the effects of kernel smoothing on copula estimation. They then propose a smoothing bandwidth selection rule based on the derived bias and variance. After confirming their theoretical findings through simulations, they use their kernel estimator to formulate a goodness‐of‐fit test for parametric copula models.
Local likelihood with time‐varying additive hazards model
Authors: Li Hui , Yin Guosheng , Zhou Yong 
Published: February 2009

AbstractThe authors propose the local likelihood method for the time‐varying coefficient additive hazards model. They use the Newton‐Raphson algorithm to maximize the likelihood into which a local polynomial expansion has been incorporated. They establish the asymptotic properties for the time‐varying coefficient estimators and derive explicit expressions for the variance and bias. The authors present simulation results describing the performance of their approach for finite sample sizes. Their numerical comparisons show the stability and efficiency of the local maximum likelihood estimator. They finally illustrate their proposal with data from a laryngeal cancer clinical study.
Robust likelihood inference for public policy
Authors: Andrews David F. 
Published: February 2009

AbstractIssues of public policy are typically decided by non‐specialists who are increasingly informed by statistical methods. In order to be influential, inferential techniques must be widely understood and accepted. This motivates the author to propose likelihood‐based methods that prove relatively insensitive to the choice of underlying distribution because they exploit a peculiarly stable relation between two standard errors and a 95% coverage probability. The author also notes that bootstrap and jackknife estimates of variance can sometimes be strongly biased. In fact, symbolic computations in R suggest that they are reliable only for statistics that are well approximated by averages whose distributions are roughly symmetric. The author thus proposes to transform the classical likelihood ratio into a statistic whose variance can be estimated robustly. He shows that the signed root of the log‐likelihood is well approximated by an average with a roughly symmetric distribution. This leads to Cox‐Tukey intervals for a Student‐like statistic and to simple confidence intervals for most models used in public policy.
Simple linear regression with multiple level shifts
Authors: Lu Qiqi , Lund Robert B. 
Published: February 2009

AbstractThe authors study the properties of the ordinary least squares trend estimator in a simple linear regression model with multiple known level shift times. The error component in the model is taken to be a general short‐memory stationary time series. The authors establish the consistency and asymptotic normality of the estimator. They also present a climatological application in which the multiple level shifts are prominent features.
Stationary state space models for longitudinal data
Authors: Jøsrgensen Bent , Song Peter X.‐K. 
Published: February 2009

AbstractThe authors consider a class of state space models for the analysis of non‐normal longitudinal data whose latent process follows a stationary AR(1) model with exponential dispersion model margins. They propose to estimate parameters through an estimating equation approach based on the Kalman smoother. This allows them to carry out a straightforward analysis of a wide range of non‐normal data. They illustrate their approach via a simulation study and through analyses of Brazilian precipitation and US polio infection data.
On inference for a semiparametric partially linear regression model with serially correlated errors
Authors: You Jinhong , Chen Gemai 
Published: February 2009

AbstractThe authors consider a semiparametric partially linear regression model with serially correlated errors. They propose a new way of estimating the error structure which has the advantage that it does not involve any nonparametric estimation. This allows them to develop an inference procedure consisting of a bandwidth selection method, an efficient semiparametric generalized least squares estimator of the parametric component, a goodness‐of‐fit test based on the bootstrap, and a technique for selecting significant covariates in the parametric component. They assess their approach through simulation studies and illustrate it with a concrete application.
General mixed‐data model: Extension of general location and grouped continuous models
Authors: De LEON Alexander R. , Carriègre K. C. 
Published: February 2009

AbstractThe authors propose a general model for the joint distribution of nominal, ordinal and continuous variables. Their work is motivated by the treatment of various types of data. They show how to construct parameter estimates for their model, based on the maximization of the full likelihood. They provide algorithms to implement it, and present an alternative estimation method based on the pairwise likelihood approach. They also touch upon the issue of statistical inference. They illustrate their methodology using data from a foreign language achievement study.
Semiparametric regression analysis of two‐sample current status data, with applications to tumorigenicity experiments
Authors: Tong Xingwei , Zhu Chao , Sun Jianguo 
Published: February 2009

AbstractCurrent status data frequently occur in failure time studies, particularly in demographical studies and tumorigenicity experiments. Although commonly used in this context, proportional hazards and odds models are inadequate when survival functions cross. The authors consider a class of two‐sample models which is suitable for this situation and encompasses the proportional hazards and odds models. The estimating equations they propose lead to consistent and asymptotically Gaussian estimates of regression parameters in the extended model. Their approach is assessed through simulations and illustrated using data from a tumorigenicity experiment.
Nonlinear functional models for functional responses in reproducing kernel hilbert spaces
Authors: Lian Heng 
Published: February 2009

AbstractThe author proposes an extension of reproducing kernel Hilbert space theory which provides a new framework for analyzing functional responses with regression models. The approach only presumes a general nonlinear regression structure, as opposed to existing linear regression models. The author proposes generalized cross‐validation for automatic smoothing parameter estimation. He illustrates the use of the new estimator both on real and simulated data.
Bayesian analysis of mark‐recapture data with travel time‐dependent survival probabilities
Authors: Muthukumarana Saman , Schwarz Carl J. , Swartz Tim B. 
Published: February 2009

AbstractThe authors extend the classical Cormack‐Jolly‐Seber mark‐recapture model to account for both temporal and spatial movement through a series of markers (e.g., dams). Survival rates are modeled as a function of (possibly) unobserved travel times. Because of the complex nature of the likelihood, they use a Bayesian approach based on the complete data likelihood, and integrate the posterior through Markov chain Monte Carlo methods. They test the model through simulations and apply it also to actual salmon data arising from the Columbia river system. The methodology was developed for use by the Pacific Ocean Shelf Tracking (POST) project.
Estimation of sex‐specific survival with uncertainty in sex assessment
Authors: Pradel Roger , Maurin‐Bernier Lory , Gimenez Olivier , Genovart Meritxell , Choquet Rémi , Oro Daniel 
Published: February 2009

AbstractIn monomorphic species, determination of sex from behavior is prone to errors. The authors develop capture‐recapture survival models that account for uncertainty in the assessment of sex. They examine parameter redundancy for four basic models with constant or time‐dependent survival and encounter probabilities. They further develop a more refined and more appropriate model for an Audouin's gull data set where four distinct behavioral clues have been used. They examine how useful it is to incorporate the least reliable of the clues and the genetic determination of sex available for only a handful of individuals. They finally discuss the implications of their findings for the design of field studies.
Automatic generation of multistate capture‐recapture models
Authors: Choquet Rémi 
Published: February 2009

AbstractNowadays multistate capture‐recapture models are used extensively in biological studies. They feature movement parameters possibly associated with the quality of sites; they also allow a combination of different sources of information to improve the stability and the accuracy of the estimates. Model refinements potentially yield more precise information on biological status. The integration of extensive biological information in the model increases its complexity, however. Thus without an appropriate tool, model building and selection may be time consuming. In this paper, the author describes a tool called GEMACO dedicated to the automatic generation of design matrices for multistate models by means of a programming language. This symbolic and flexible tool avoids tedious matrix manipulations. It is well suited for the description of complex structures and has been implemented in a generic multistate program called M‐SURGE freely available at http://www.cefe.cnrs.fr/BIOM/logiciels.htm
Analysis of band‐recovery data in a multistate capture‐recapture framework
Authors: Gauthier Gilles , Lebreton Jean‐Dominique 
Published: February 2009

AbstractDead recoveries of marked animals are commonly used to estimate survival probabilities. Band‐recovery models can be parameterized either by r (the probability of recovering a band conditional on death of the animal) or by f (the probability that an animal will be killed, retrieved, and have its band reported). The T parametrization can be implemented in a capture‐recapture framework with two states (alive and newly dead), mortality being the transition probability between the two states. The authors show here that the f parametrization can also be implemented in a multistate framework by imposing simple constraints on some parameters. They illustrate it using data on the mallard and the snow goose. However, they mention that because it does not entirely separate the individual survival and encounter processes, the f parametrization must be used with care on reduced models, or in the presence of estimates at the boundary of the parameter space. As they show, a multistate framework allows the use of powerful software for model fitting or testing the goodness‐of‐fit of models; it also affords the implementation of complex models such as those based on mixture of information or uncertain states
Why a time effect often has a limited impact on capture‐recapture estimates in closed populations
Authors: Rivest Louis‐Paul 
Published: February 2009

AbstractThe author is concerned with log‐linear estimators of the size N of a population in a capture‐recapture experiment featuring heterogeneity in the individual capture probabilities and a time effect. He also considers models where the first capture influences the probability of subsequent captures. He derives several results from a new inequality associated with a dispersive ordering for discrete random variables. He shows that in a log‐linear model with inter‐individual heterogeneity, the estimator N is an increasing function of the heterogeneity parameter. He also shows that the inclusion of a time effect in the capture probabilities decreases N in models without heterogeneity. He further argues that a model featuring heterogeneity can accommodate a time effect through a small change in the heterogeneity parameter. He demonstrates these results using an inequality for the estimators of the heterogeneity parameters and illustrates them in a Monte Carlo experiment
The potential of integrated modelling in conservation biology: A case study of the black‐footed albatross (Phoebastria nigripes)
Authors: Véran Sophie , Lebreton Jean‐Dominique 
Published: February 2009

AbstractConservation biology aims at assessing the status of a population, based on information which is often incomplete. Integrated population modelling based on state‐space models appears to be a powerful and relevant way of combining into a single likelihood several types of information such as capture‐recapture data and population surveys. In this paper, the authors describe the principles of integrated population modelling and they evaluate its performance for conservation biology based on a case study, that of the black‐footed albatross, a northern Pacific albatross species suspected to be impacted by longline fishing
Nonparametric tests of hypotheses for umbrella alternatives
Authors: Alvo Mayer 
Published: February 2009

AbstractThe author proposes a general method for constructing nonparametric tests of hypotheses for umbrella alternatives. Such alternatives are relevant when the treatment effect changes in direction after reaching a peak. The author's class of tests is based on the ranks of the observations. His general approach consists of defining two sets of rankings: the first is induced by the alternative and the other by the data itself. His test statistic measures the distance between the two sets. The author determines the asymptotic distribution for some special cases of distances under both the null and the alternative hypothesis when the location of the peak is known or unknown. He shows the good power of his tests through a limited simulation study
Likelihood and bayesian approaches to inference for the stationary point of a quadratic response surface
Authors: Sambucini Valeria , Piccinato Ludovico 
Published: February 2009

AbstractIn response surface analysis, a second order polynomial model is often used for inference on the stationary point of the response function. The standard confidence regions for the stationary point are due to Box & Hunter (1954). The authors propose an alternative parametrization, in which the stationary point is the parameter of interest; likelihood techniques and Bayesian analysis are then easier to perform. The authors also suggest an approximate method to get highest posterior density regions for the maximum point (not simply for the stationary point). Furthermore, they study the coverage probabilities of these Bayesian regions through simulations.
Robust methods for generalized linear models with nonignorable missing covariates
Authors: Sinha Sanjoy K. 
Published: February 2009

AbstractThe EM algorithm is often used for finding the maximum likelihood estimates in generalized linear models with incomplete data. In this article, the author presents a robust method in the framework of the maximum likelihood estimation for fitting generalized linear models when nonignorable covariates are missing. His robust approach is useful for downweighting any influential observations when estimating the model parameters. To avoid computational problems involving irreducibly high‐dimensional integrals, he adopts a Metropolis‐Hastings algorithm based on a Markov chain sampling method. He carries out simulations to investigate the behaviour of the robust estimates in the presence of outliers and missing covariates; furthermore, he compares these estimates to the classical maximum likelihood estimates. Finally, he illustrates his approach using data on the occurrence of delirium in patients operated on for abdominal aortic aneurysm.
Inference for general parametric functions in box‐Cox‐type transformation models
Authors: Yang Zhenlin , Wu Eden Ka‐Ho , Desmond Anthony F. 
Published: February 2009

AbstractThe authors propose a simple but general method of inference for a parametric function of the Box‐Cox‐type transformation model. Their approach is built upon the classical normal theory but takes parameter estimation into account. It quickly leads to test statistics and confidence intervals for a linear combination of scaled or unsealed regression coefficients, as well as for the survivor function and marginal effects on the median or other quantité functions of an original response. The authors show through simulations that the finite‐sample performance of their method is often superior to the delta method, and that their approach is robust to mild departures from normality of error distributions. They illustrate their approach with a numerical example.
A Simplified Clinical Electrocardiogram Score for the Prediction of Cardiovascular Mortality
Authors: Tan Swee Yaw , Sungar Gannon W , Myers Jonathan , Sandri Marcus , Froelicher Victor 
Published: February 2009

AbstractBackgroundElectrocardiogram (ECG) scores have been demonstrated to predict CV mortality but they are rarely utilized clinically.ObjectiveDevelop a simple score consisting of adding classical ECG abnormalities to make the ECG a more convenient prognostic tool.MethodsResting ECGs of 29,320 outpatient male veterans from the Palo Alto Veteran Affairs Healthcare System (PAVHS) collected between 1987 and 2000 were computer analyzed with an average follow‐up of 7.5 y. Twelve classic ECG abnormalities were chosen on the basis of prevalence and corresponding relative risks, including left and right bundle branch block, diagnostic Q waves, intraventricular conduction defect, atrial fibrillation, left atrial abnormality, left and right axis deviation, left and right ventricular hypertrophy, ST depression, and abnormal QTc interval. A simple score derived from the summation of these criteria was then entered into an age and heart rate adjusted Cox analysis.ResultsThere was a progressive increase in risk of death as the number of ECG abnormalities increased. The relative risks for 1, 2, 3, 4, and 5 ECG abnormalities were 1.8 (CI 1.6–2.0), 2.4 (CI 2.2–2.7), 3.6 (CI 3.2–4.1), 4.5 (CI 3.8–5.4), and 6.0 (CI 4.7–7.8) respectively (p < 0.001). The age‐adjusted hazard ratio for CV mortality was 6.0 when there were five or more ECG abnormalities present.ConclusionSumming the number of classical ECG abnormalities provides a powerful predictor of CV mortality independent of age, standard risk factors, and clinical status. Copyright © 2009 Wiley Periodicals, Inc.