bluuebunny/crossref_metadata_2025_split huggingface dataset:
Percutaneous closure of patent foramen ovale with a bioabsorbable occluder device
Authors: Ussia Gian Paolo , Cammalleri Valeria , Mulè Massimiliano , Scarabelli Marilena , Barbanti Marco , Scardaci Francesco , Mangiafico Sarah , Immè Sebastiano , Capodanno Davide , Tamburino Corrado 
Published: February 2009

AbstractBackground:Percutaneous closure of patent foramen ovale (PFO) is routinely performed with nonbiological devices, characterized by a persistent low‐grade inflammatory response. We report our experience about PFO closure with a bioabsorbable device, BioSTAR® (NMT Inc, USA).Methods:From September 2007 to September 2008, 14 patients with migraine (eight with aura) and cerebral magnetic resonance positive for silent ischemia and nine patients with prior cardiovascular accident (CVA) underwent closure of PFO using BioSTAR®. One patient had heterozygosis for sickle‐cell‐anaemia. Nickel allergy was present in eight patients. Echocardiogram was performed at 24 hr, one and 6 months. At 6 and 12 months a contrast‐transcranial‐doppler (c‐TCD) and a trans‐oesophageal echocardiogram (TOE) were scheduled, respectively.Results:BioSTAR® was successfully implanted in 22 patients (96%). The mean procedural time and the mean fluoroscopy time were 22 ± 6 and 4 ± 2 minutes, respectively. The mean in‐hospital stay was 3 ± 0.5 days. After a mean follow‐up of 7.8 ± 3.5 months there was an hemorrhagic stroke related to double antiaggregation. No other CVA or allergic reactions were registered. There were two cases of atrial arrhythmia. Fifteen patients had not residual shunts at c‐TCD, while in four patients we observed a trivial microbubbles passage. The TOE, achieved in nine patients without contrast, showed the device well positioned, with a low profile and without thrombus.Conclusions:In our experience PFO closure with BioSTAR® is safe and efficacious in preventing recurrent CVA. Its use could be advantageous in patients with nickel allergy and haematological disorders. The potential benefits of this device need to be certified in a larger cohort of patients with a longer follow‐up. © 2009 Wiley‐Liss, Inc.
Magnetic wire lock: Prevention and correction to avoid wire fracture
Authors: Hilst Kwame van der , Patterson Mark S. 
Published: February 2009

AbstractThis report illustrates an undescribed mechanism causing magnetic angioplasty wire fracture and describes how to avoid its occurrence and how to correct it, if it occurs. The cause is the transition point in the wire between a flexible wire shaft and the inflexible magnetic tip of a magnetically enabled wire. In the first stage of the problem, the wire becomes trapped in a doubled‐back position. Subsequently, traction causes the second stage that kinks the wire and hooks behind a structure, in this case, a stent, causing magnetic wire lock. Further traction has a high chance of wire fracture. Correction, although challenging, remains possible before wire fracture occurs. Although there is no intrinsic problem with the technology, attention to this problem by the operator will reduce its occurrence and therefore its sequelae. © 2009 Wiley‐Liss, Inc.
Survival analysis based on the proportional hazards model and survey data
Authors: Boudreau Christian , Lawless Jerald F. 
Published: February 2009

AbstractThe authors propose methods based on the stratified Cox proportional hazards model that account for the fact that the data have been collected according to a complex survey design. The methods they propose are based on the theory of estimating equations in conjunction with empirical process theory. The authors also discuss issues concerning ignorable sampling design, and the use of weighted and unweighted procedures. They illustrate their methodology by an analysis of jobless spells in Statistics Canada's Survey of Labour and Income Dynamics. They discuss briefly problems concerning weighting, model checking, and missing or mismeasured data. They also identify areas for further research.
A class of sequential tests for two‐sample composite hypotheses
Authors: Gombay Edit , Hussein Abdulkadir 
Published: February 2009

AbstractThe authors propose a class of statistics based on Rao's score for the sequential testing of composite hypotheses comparing two treatments (populations). Asymptotic approximations of the statistics lead them to propose sequential tests and to derive their monitoring boundaries. As special cases, they construct sequential versions of the two‐sample t‐test for normal populations and two‐sample z‐score tests for binomial populations. The proposed algorithms are simple and easy to compute, as no numerical integration is required. Furthermore, the user can analyze the data at any time regardless of how many inspections have been made. Monte Carlo simulations allow the authors to compare the power and the average stopping time (also known as average sample number) of the proposed tests to those of nonsequential and group sequential tests. A two‐armed comparative clinical trial in patients with adult leukemia allows them to illustrate the efficiency of their methods in the case of binary responses.
Estimation of regression parameters in missing data problems
Authors: Mcleish Donald L. , Struthers Cyntha A. 
Published: February 2009

AbstractLetYbe a response variable, possibly multivariate, with a density functionf(y|x,v; β) conditional on vectorsxandvof covariates and a vector β of unknown parameters. The authors consider the problem of estimatingβwhen the values taken by the covariate vectorvare available for all observations while some of those taken by the covariatexare missing at random. They compare the profile estimator to several alternatives, both in terms of bias and standard deviation, when the response and covariates are discrete or continuous.
Robust inference in generalized linear models for longitudinal data
Authors: Sinha Sanjoy K. 
Published: February 2009

AbstractThe author develops a robust quasi‐likelihood method, which appears to be useful for down‐weighting any influential data points when estimating the model parameters. He illustrates the computational issues of the method in an example. He uses simulations to study the behaviour of the robust estimates when data are contaminated with outliers, and he compares these estimates to those obtained by the ordinary quasi‐likelihood method.
Approximating bayesian inference by weighted likelihood
Authors: Wang Xiaogang 
Published: February 2009

AbstractThe author proposes to use weighted likelihood to approximate Bayesian inference when no external or prior information is available. He proposes a weighted likelihood estimator that minimizes the empirical Bayes risk under relative entropy loss. He discusses connections among the weighted likelihood, empirical Bayes and James‐Stein estimators. Both simulated and real data sets are used for illustration purposes.
A bayesian signal detection procedure for scale‐space random fields
Authors: Rohani M. Farid , Shafie Khalil , Noorbaloochi Siamak 
Published: February 2009

AbstractThe authors consider the problem of searching for activation in brain images obtained from functional magnetic resonance imaging and the corresponding functional signal detection problem. They develop a Bayesian procedure to detect signals existing within noisy images when the image is modeled as a scale space random field. Their procedure is based on the Radon‐Nikodym derivative, which is used as the Bayes factor for assessing the point null hypothesis of no signal. They apply their method to data from the Montreal Neurological Institute.
Correlation analysis of ordered symmetrically dependent observations and their concomitants of order statistics
Authors: Viana Marios A. G. , Lee Hak‐Myung 
Published: February 2009

AbstractGiven two jointly observed random vectors Y and Z of the same dimension, let Y be a reordered version of Y and Z the resulting vector of concomitants of order statistics. When X is a covariate of interest, also jointly observed with Y, the authors obtain the joint covariance structure of (X, y, Z) and the related correlation parameters explicitly, under the assumption that the vector (X, Y, Z) is normal and that its joint covariance structure is permutation symmetric. They also discuss extensions to elliptically contoured distributions.
Pseudo‐likelihood estimation in ARCH models
Authors: Mukherjee Kanchan 
Published: February 2009

AbstractThe author presents asymptotic results for the class of pseudo‐likelihood estimators in the autoregressive conditional heteroscedastic models introduced by Engle (1982). Unlike what is required for the quasi‐likelihood estimator, some estimators in the class he considers do not require the finiteness of the fourth moment of the error density. Thus his method is applicable to heavy‐tailed error distributions for which moments higher than two may not exist.
Pseudo‐empirical likelihood ratio confidence intervals for complex surveys
Authors: Wu Changbao , Rao J. N. K. 
Published: February 2009

AbstractThe authors show how an adjusted pseudo‐empirical likelihood ratio statistic that is asymptotically distributed as a chi‐square random variable can be used to construct confidence intervals for a finite population mean or a finite population distribution function from complex survey samples. They consider both non‐stratified and stratified sampling designs, with or without auxiliary information. They examine the behaviour of estimates of the mean and the distribution function at specific points using simulations calling on the Rao‐Sampford method of unequal probability sampling without replacement. They conclude that the pseudo‐empirical likelihood ratio confidence intervals are superior to those based on the normal approximation, whether in terms of coverage probability, tail error rates or average length of the intervals.
Conservative prior distributions for variance parameters in hierarchical models
Authors: Gustafson Paul , Hossain Shahadut , Macnab Ying C. 
Published: February 2009

AbstractBayesian hierarchical models typically involve specifying prior distributions for one or more variance components. This is rather removed from the observed data, so specification based on expert knowledge can be difficult. While there are suggestions for “default” priors in the literature, often a conditionally conjugate inverse‐gamma specification is used, despite documented drawbacks of this choice. The authors suggest “conservative” prior distributions for variance components, which deliberately give more weight to smaller values. These are appropriate for investigators who are skeptical about the presence of variability in the second‐stage parameters (random effects) and want to particularly guard against inferring more structure than is really present. The suggested priors readily adapt to various hierarchical modelling settings, such as fitting smooth curves, modelling spatial variation and combining data from multiple sites.
On describing multivariate skewed distributions: A directional approach
Authors: Ferreira José T. A. S. , Steel Mark F. J. 
Published: February 2009

AbstractMost multivariate measures of skewness in the literature measure the overall skewness of a distribution. These measures were designed for testing the hypothesis of distributional symmetry; their relevance for describing skewed distributions is less obvious. In this article, the authors consider the problem of characterizing the skewness of multivariate distributions. They define directional skewness as the skewness along a direction and analyze two parametric classes of skewed distributions using measures based on directional skewness. The analysis brings further insight into the classes, allowing for a more informed selection of classes of distributions for particular applications. The authors use the concept of directional skewness twice in the context of Bayesian linear regression under skewed error: first in the elicitation of a prior on the parameters of the error distribution, and then in the analysis of the skewness of the posterior distribution of the regression residuals.
Strong orthogonal decompositions and non‐linear impulse response functions for infinite‐variance processes
Authors: Hill Jonathan B. 
Published: February 2009

AbstractThe author proves that Wold‐type decompositions with strong orthogonal prediction innovations exist in smooth, reflexive Banach spaces of discrete time processes if and only if the projection operator generating the innovations satisfies the property of iterations. His theory includes as special cases all previous Wold‐type decompositions of discrete time processes, completely characterizes when non‐linear heavy‐tailed processes obtain a strong‐orthogonal moving average representation, and easily promotes a theory of non‐linear impulse response functions for infinite‐variance processes. The author exemplifies his theory by developing a non‐linear impulse response function for smooth transition threshold processes, and discusses how to test decomposition innovations for strong orthogonality and whether the proposed model represents the best predictor. A data set on currency exchange rates allows him to illustrate his methodology.
A unified view on skewed distributions arising from selections
Authors: Arellano‐Valle Reinaldo B. , Branco Máaarcia D. , Genton Marc G. 
Published: February 2009

AbstractParametric families of multivariate nonnormal distributions have received considerable attention in the past few decades. The authors propose a new definition of a selection distribution that encompasses many existing families of multivariate skewed distributions. Their work is motivated by examples that involve various forms of selection mechanisms and lead to skewed distributions. They give the main properties of selection distributions and show how various families of multivariate skewed distributions, such as the skew‐normal and skew‐elliptical distributions, arise as special cases. The authors further introduce several methods of constructing selection distributions based on linear and nonlinear selection mechanisms.
Some robust designs for polynomial regression models
Authors: Fang Zhide 
Published: February 2009

AbstractThe author identifies static optimal designs for polynomial regression models with or without intercept. His optimality criterion is an average between the D‐optimality criterion for the estimation of low‐degree terms and the D8‐optimality criterion for testing the significance of higher degree terms. His work relies on classical results concerning canonical moments and the theory of continued fractions.
Multiple imputation methods for recurrent event data with missing event category
Authors: Schaubel Douglas E. , Cai Jianwen 
Published: February 2009

AbstractFrequently in clinical and epidemiologic studies, the event of interest is recurrent (i.e., can occur more than once per subject). When the events are not of the same type, an analysis which accounts for the fact that events fall into different categories will often be more informative. Often, however, although event times may always be known, information through which events are categorized may potentially be missing. Complete‐case methods (whose application may require, for example, that events be censored when their category cannot be determined) are valid only when event categories are missing completely at random. This assumption is rather restrictive. The authors propose two multiple imputation methods for analyzing multiple‐category recurrent event data under the proportional means/rates model. The use of a proper or improper imputation technique distinguishes the two approaches. Both methods lead to consistent estimation of regression parameters even when the missingness of event categories depends on covariates. The authors derive the asymptotic properties of the estimators and examine their behaviour in finite samples through simulation. They illustrate their approach using data from an international study on dialysis.
Classification with reject option
Authors: Herbei Radu , Wegkamp Marten H. 
Published: February 2009

AbstractThe authors study binary classification that allows for a reject option in which case no decision is made. This reject option is to be used for those observations for which the conditional class probabilities are close and as such are hard to classify. The authors generalize existing theory for both plug‐in rules and empirical risk minimizers to this setting.
On consistent testing for serial correlation in seasonal time series models
Authors: Duchesne Pierre 
Published: February 2009

AbstractThe author considers serial correlation testing in seasonal time series models. He proposes a test statistic based on a spectral approach. Many tests of this type rely on kernel‐based spectral density estimators that assign larger weights to low order lags than to high ones. Under seasonality, however, large autocorrelations may occur at seasonal lags that classical kernel estimators cannot take into account. The author thus proposes a test statistic that relies on the spectral density estimator of Shin (2004), whose weighting scheme is more adapted to this context. The distribution of his test statistic is derived under the null hypothesis and he studies its behaviour under fixed and local alternatives. He establishes the consistency of the test under a general fixed alternative. He also makes recommendations for the choice of the smoothing parameters. His simulation results suggest that his test is more powerful against seasonality than alternative procedures based on classical weighting schemes. He illustrates his procedure with monthly statistics on employment among young Americans.
On the multivariate spatial median for clustered data
Authors: Nevalainen Jaakko , Larocque Denis , Oja Hannu 
Published: February 2009

AbstractThe authors consider the multivariate one‐sample location problem with clustered data from a nonparametric viewpoint. They propose the spatial median and its affine equivariant version as companion estimators to the affine invariant sign test of Larocque (2003). They extend the asymptotics of the proposed estimators to cluster dependent data and explore the limiting as well as finite‐sample efficiencies for multivariate Student distributions. They demonstrate that the efficiency of the spatial median suffers less from intracluster correlation than the mean vector. They use data on the well‐being of pupils in Finnish schools to illustrate their work.
A unified approach to estimation of nonlinear mixed effects and Berkson measurement error models
Authors: Wang Liqun 
Published: February 2009

AbstractMixed effects models and Berkson measurement error models are widely used. They share features which the author uses to develop a unified estimation framework. He deals with models in which the random effects (or measurement errors) have a general parametric distribution, whereas the random regression coefficients (or unobserved predictor variables) and error terms have nonparametric distributions. He proposes a second‐order least squares estimator and a simulation‐based estimator based on the first two moments of the conditional response variable given the observed covariates. He shows that both estimators are consistent and asymptotically normally distributed under fairly general conditions. The author also reports Monte Carlo simulation studies showing that the proposed estimators perform satisfactorily for relatively small sample sizes. Compared to the likelihood approach, the proposed methods are computationally feasible and do not rely on the normality assumption for random effects or other variables in the model.
Objective Bayesian analysis of spatial data with measurement error
Authors: De Oliveira Victor 
Published: February 2009

AbstractThe author shows how geostatistical data that contain measurement errors can be analyzed objectively by a Bayesian approach using Gaussian random fields. He proposes a reference prior and two versions of Jeffreys' prior for the model parameters. He studies the propriety and the existence of moments for the resulting posteriors. He also establishes the existence of the mean and variance of the predictive distributions based on these default priors. His reference prior derives from a representation of the integrated likelihood that is particularly convenient for computation and analysis. He further shows that these default priors are not very sensitive to some aspects of the design and model, and that they have good frequentist properties. Finally, he uses a data set of carbon/nitrogen ratios from an agricultural field to illustrate his approach.
Objective priors for hypothesis testing in one‐way random effects models
Authors: García‐Donato Gonzalo , Sun Dongchu 
Published: February 2009

AbstractThe Bayes factor is a key tool in hypothesis testing. Nevertheless, the important issue of which priors should be used to develop objective Bayes factors remains open. The authors consider this problem in the context of the one‐way random effects model. They use concepts such as orthogonality, predictive matching and invariance to justify a specific form of the priors for common parameters and derive the intrinsic and divergence based prior for the new parameter. The authors show that both intrinsic priors or divergence‐based priors produce consistent Bayes factors. They illustrate the methods and compare them with other proposals.
Asymptotic properties of likelihood ratio test statistics in affected‐sib‐pair analysis
Authors: Feng Zeny Z. , Chen Jiahua , Thompson Mary E. 
Published: February 2009

AbstractIn an affected‐sib‐pair genetic linkage analysis, identical by descent data for affected sib pairs are routinely collected at a large number of markers along chromosomes. Under very general genetic assumptions, the IBD distribution at each marker satisfies the possible triangle constraint. Statistical analysis of IBD data should thus utilize this information to improve efficiency. At the same time, this constraint renders the usual regularity conditions for likelihood‐based statistical methods unsatisfied. In this paper, the authors study the asymptotic properties of the likelihood ratio test (LRT) under the possible triangle constraint. They derive the limiting distribution of the LRT statistic based on data from a single locus. They investigate the precision of the asymptotic distribution and the power of the test by simulation. They also study the test based on the supremum of the LRT statistics over the markers distributed throughout a chromosome. Instead of deriving a limiting distribution for this test, they use a mixture of chi‐squared distributions to approximate its true distribution. Their simulation results show that this approach has desirable simplicity and satisfactory precision.
Optimal designs for calibration of orientations
Authors: Shin Hwashin H. , Takahara Glen , Murdoch Duncan J. 
Published: February 2009

AbstractThis paper concerns designed experiments involving observations of orientations following the models of Prentice (1989) and Rivest &Chang (2006). The authors state minimal conditions on the designs for consistent least squares estimation of the matrix parameters in these models. The conditions are expressed in terms of the axes and rotation angles of the design orientations. The authors show that designs satisfyingU1+ … +Un= 0 are optimal in the sense of minimizing the estimation error average angular distance. The authors give constructions of optimaln‐point designs whenn≥ 4 and they compare the performance of several designs through approximations and simulation.