bluuebunny/crossref_metadata_2025_split huggingface dataset:
Penalized regression with model‐based penalties
Authors: Heckman Nancy E. , Ramsay James O. 
Published: June 2006

AbstractNonparametric regression techniques such as spline smoothing and local fitting depend implicitly on a parametric model. For instance, the cubic smoothing spline estimate of a regression function ∫ μ based on observationsti,Yi is the minimizer of Σ{Yi ‐ μ(ti)}2+ λ∫(μ′′)2. Since ∫(μ″)2is zero when μ is a line, the cubic smoothing spline estimate favors the parametric model μ(t) = αo+ α1t. Here the authors consider replacing ∫(μ″)2with the more general expression ∫(Lμ)2whereLis a linear differential operator with possibly nonconstant coefficients. The resulting estimate of μ performs well, particularly ifLμ is small. They present an O(n) algorithm for the computation of μ. This algorithm is applicable to a wide class ofL's. They also suggest a method for the estimation ofL. They study their estimates via simulation and apply them to several data sets.
Regression residuals and test statistics: Assessing naive outlier deletion
Authors: Dupuis Debbie J. , Hamilton David C. 
Published: June 2006

AbstractThe authors derive the joint distributions of a studentized deleted residual and various regression quantities, calculated with all the data or with one case deleted. They show that the correlation between the studentized deleted residual and the deleted test statistic has an interesting interpretation in terms of well‐known regression quantities. These results allow them to examine the effect of applying some naive outlier deletion methods before making inferences about a regression parameter.
Robust estimation of the SUR model
Authors: Bilodeau Martin , Duchesne Pierre 
Published: June 2006

AbstractThis paper proposes robust regression to solve the problem of outliers in seemingly unrelated regression (SUR) models. The authors present an adaptation of S‐estimators to SUR models. S‐estimators are robust, have a high breakdown point and are much more efficient than other robust regression estimators commonly used in practice. Furthermore, modifications to Ruppert's algorithm allow a fast evaluation of them in this context. The classical example of U.S. corporations is revisited, and it appears that the procedure gives an interesting insight into the problem.
Bayesian prediction of waiting times in stochastic models
Authors: Butler Ronald W. , Huzurbazar Aparna V. 
Published: June 2006

AbstractThe authors show how saddlepoint techniques lead to highly accurate approximations for Bayesian predictive densities and cumulative distribution functions in stochastic model settings where the prior is tractable, but not necessarily the likelihood or the predictand distribution. They consider more specifically models involving predictions associated with waiting times for semi‐Markov processes whose distributions are indexed by an unknown parameter θ. Bayesian prediction for such processes when they are not stationary is also addressed and the inverse‐Gaussian based saddlepoint approximation of Wood, Booth & Butler (1993) is shown to accurately deal with the nonstationarity whereas the normal‐based Lugannani & Rice (1980) approximation cannot, Their methods are illustrated by predicting various waiting times associated withM/M/qandM/G/1queues. They also discuss modifications to the matrix renewal theory needed for computing the moment generating functions that are used in the saddlepoint methods.
Sensitivity of the fractional Bayes factor to prior distributions
Authors: Conigliani Caterina , O'hagan Anthony 
Published: June 2006

AbstractThe authors derive a measure of the sensitivity of the fractional Bayes factor, an index which is used to compare models when the priors for their respective parameters are improper, or when there is concern about robustness of the prior specification. They prove that in a large class of problems, this measure is a decreasing function of the fraction of the sample used to update the prior distribution before the models are compared.
Reconstructing the history of forest fire frequency: Identifying hazard rate change points using the bayes information criterion
Authors: Reed William J. 
Published: June 2006

AbstractA problem in model selection, namely the identification of multiple change points for a piece‐wise constant hazard rate, is discussed. A methodology using the Bayes Information Criterion is developed in an overdispersed survival model (with corresponding quasi‐likelihood function). The technique is used to identify changes in the historical frequency of forest fire. It is applied to two datasets derived from time‐since‐fire maps in the Canadian Rockies.
Modeling of rates over a hierarchical health administrative structure
Authors: Dean Charmaine B. , Macnab Ying Cai 
Published: June 2006

AbstractIdentifying the distribution of the incidence rate of a disease over a region is a prediction problem where area‐specific random effects are to be estimated. The authors consider the inclusion of such effects at different levels of a hierarchical health administrative structure. They develop inference procedures for this type of multi‐level model and show that the predicted rates are approximately weighted sums of the crude rates obtained by pooling data on each level of the hierarchy. Their techniques are illustrated using infant mortality data from British Columbia.
Survival analysis with long‐term survivors and partially observed covariates
Authors: Cho Meehyung , Schenker Nathaniel , Taylor Jeremy M. G. , Zhuang Dongliang 
Published: June 2006

AbstractThe authors describe a method for fitting failure time mixture models that postulate the existence of both susceptibles and long‐term survivors when covariate data are only partially observed. Their method is based on a joint model that combines a Weibull regression model for the susceptibles, a logistic regression model for the probability of being a susceptible, and a general location model for the distribution of the covariates. A Bayesian approach is taken, and Gibbs sampling is used to fit the model to the incomplete data. An application to clinical data on tonsil cancer and a small Monte Carlo study indicate potential large gains in efficiency over standard complete‐case analysis as well as reasonable performance in a variety of situations.
A new approach to default priors and robust bayes methodology
Authors: Delampady Mohan , Dasgupta Anirban , Casella George , Rubin Herman , Strawderman William E. 
Published: June 2006

AbstractFollowing the developments in DasGupta et al. (2000), the authors propose and explore a new method for constructing proper default priors and a method for selecting a Bayes estimate from a family. Their results are based on asymptotic expansions of certain marginal correlations. For ease of exposition, most results are presented for location families and squared error loss only. The default prior methodology amounts, ultimately, to the minimization of Fisher information, and hence, Bickel's prior works out as the default prior if the location parameter is bounded. As for the selected Bayes estimate, it corresponds to ‘Gaussian tilting’ of an initial reference prior.
On the application of extended quasi‐likelihood to the clustered data case
Authors: Hall Daniel B. 
Published: June 2006

AbstractThe author describes the relationship between the extended generalized estimating equations (EGEEs) of Hall & Severini (1998) and various similar methods. He proposes a true extended quasi‐likelihood approach for the clustered data case and explores restricted maximum likelihood‐like versions of the EGEE and extended quasi‐likelihood estimating equations. He also presents simulation results comparing the various estimators in terms of mean squared error of estimation based on three moderate sample size, discrete data situations.
Estimating the order of a hidden markov model
Authors: Mackay Rachel J. 
Published: June 2006

AbstractWhile the estimation of the parameters of a hidden Markov model has been studied extensively, the consistent estimation of the number of hidden states is still an unsolved problem. The AIC and BIC methods are used most commonly, but their use in this context has not been justified theoretically. The author shows that for many common models, the penalized minimum‐distance method yields a consistent estimate of the number of hidden states in a stationary hidden Markov model. In addition to addressing the identifiability issues, she applies her method to a multiple sclerosis data set and assesses its performance via simulation.
Testing circular symmetry
Authors: Pewsey Arthur 
Published: June 2006

AbstractThe author addresses the problem of testing circular data for reflective symmetry about an unknown central direction and proposes a simple omnibus test based on the sample second sine moment about an estimation of this direction. Under quite general conditions, for an underlying distribution which is reflectively symmetric, the large‐sample asymptotic distribution of the test statistic is standard normal. Randomization and bootstrap variants of the test are also introduced, and the operating characteristics of different versions of the test are investigated in a Monte Carlo study. The large‐sample and bootstrap versions of the test are applied in the analysis of two illustrative examples drawn from the circular statistics literature.
Bayesian computation for the superposition of nonhomogeneous poisson processes
Authors: Yang Tae Young , Kuo Lynn 
Published: June 2006

AbstractBayesian inference for the superposition of nonhomogeneous Poisson processes is studied. A Markov‐chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, a latent variable is introduced that indicates which component of the superposition model gives rise to the failure. This data‐augmentation approach facilitates specification of the transitional kernel in the Markov chain. Moreover, new Bayesian tests are developed for the full superposition model against simpler submodels. Model determination by a predictive likelihood approach is studied. A numerical example based on a real data set is given.
Some linear model techniques for analyzing small‐circle spherical data
Authors: Rivest Louis‐Paul 
Published: June 2006

AbstractThe author investigates least squares as a method for fitting small‐circle models to a sample of unit vectors in R3. He highlights a local linear model underlying the estimation of the parameters of a circle. This model is used to construct an estimation algorithm and regression‐type inference procedures for the parameters of a circle. It makes it possible to compare the fit of a small circle with that of a spherical ellipse. The limitations of the least‐squares approach are emphasized: when the errors are bounded away from 0, the least‐squares estimators are not consistent as the sample size goes to infinity. Two examples, concerned with the migration of elephant seals and with the classification of geological folds, are analyzed using the linear model techniques proposed in this work.
Convergence properties of the EM algorithm in constrained parameter spaces
Authors: Nettleton Dan 
Published: June 2006

AbstractThe established general results on convergence properties of the EM algorithm require the sequence of EM parameter estimates to fall in the interior of the parameter space over which the likelihood is being maximized. This paper presents convergence properties of the EM sequence of likelihood values and parameter estimates in constrained parameter spaces for which the sequence of EM parameter estimates may converge to the boundary of the constrained parameter space contained in the interior of the unconstrained parameter space. Examples of the behavior of the EM algorithm applied to such parameter spaces are presented.
A minimally informative likelihood for decision analysis: Illustration and robustness
Authors: Yuan Ao , Clarke Bertrand S. 
Published: June 2006

AbstractThe authors discuss a class of likelihood functions involving weak assumptions on data generating mechanisms. These likelihoods may be appropriate when it is difficult to propose models for the data. The properties of these likelihoods are given and it is shown how they can be computed numerically by use of the Blahut‐Arimoto algorithm. The authors then show how these likelihoods can give useful inferences using a data set for which no plausible physical model is apparent. The plausibility of the inferences is enhanced by the extensive robustness analysis these likelihoods permit.
La revue canadiennede statistique statistical science: Concepts, opportunities and challenges
Authors: Lawless Jerald F. 
Published: June 2006

AbstractAdvances in computing and information technology have had a profound impact on many areas of human activity. Statistical tools are ever more widely used, often in new and unconventional environments. Opportunities for statisticians have never been greater, and at the same time, the use of statistics by others is increasing. The author discusses challenges which arise from these conditions, with emphasis on the identity, activities and education of statisticians.
Robust extrapolation designs and weights for biased regression models with heteroscedastic errors
Authors: Fang Zhide , Wiens Douglas P. 
Published: June 2006

AbstractWe consider the construction of designs for the extrapolation of regression responses, allowing both for possible heteroscedasticity in the errors and for imprecision in the specification of the response function. We find minimax designs and correspondingly optimal estimation weights in the context of the following problems: (1) for ordinary least squares estimation, determine a design to minimize the maximum value of the integrated mean squared prediction error (IMSPE), with the maximum being evaluated over both types of departure; (2) for weighted least squares estimation, determine both weights and a design to minimize the maximum IMSPE; (3) choose weights and design points to minimize the maximum IMSPE, subject to a side condition of unbiasedness. Solutions to (1) and (2) are given for multiple linear regression with no interactions, a spherical design space and an annular extrapolation space. For (3) the solution is given in complete generality; as one example we consider polynomial regression. Applications to a dose‐response problem for bioassays are discussed. Numerical comparisons, including a simulation study, indicate that, as well as being easily implemented, the designs and weights for (3) perform as well as those for (1) and (2) and outperform some common competitors for moderate but undetectable amounts of model bias.
L'analyse de la variation saisonniére quand l'amplitude et la taille sont faibles
Authors: Marrero Osvaldo 
Published: June 2006

AbstractStudies of seasonal variation are valuable in biomedical research because they can help to discover the etiology of diseases that are not well understood. Generally in these studies the data have certain characteristics that require specialized tests and methods for the statistical analysis. But the effectiveness of these specialized tests is variable, especially according to the seasonal variation, the dimension of the amplitude in the seasonal variation, and the sample size. The purpose of this paper is to present a test and methods appropriate for the analysis and modeling of data whose seasonal variation has small amplitude and whose sample size is small. This test can detect different kinds of seasonal variation. The results from a simulation study show that the test performs very well. The application of these methods is illustrated by two examples.
Methodological agreement between two‐compartment body‐composition methods in children
Authors: Buison Anne M. , Ittenbach Richard F. , Stallings Virginia A. , Zemel Babette S. 
Published: June 2006

AbstractIncreases in childhood obesity have emphasized the importance of accurate and accessible body composition assessment, especially in monitoring prevention and treatment efforts. Previous pediatric studies, comparing measures from air‐displacement plethysmography (ADP) to dual‐energy X‐ray absorptiometry (DXA) and anthropometry (ANTH, skinfold measures), were performed in small numbers of children or in children across large age and body‐size ranges. The objectives of this study were: 1) to compare body fat percentage (%BF), fat mass (FM), and fat‐free mass (FFM) from ADP with DXA and ANTH, to determine the agreement between techniques; 2) to identify factors that influence agreement or lack of agreement; and 3) to determine if the agreement is constant over a range of body fatness. Healthy children (n = 125), 7–10 years old, participating in a longitudinal pediatric bone health study, were evaluated. Body composition was assessed by ADP, DXA, and ANTH to determine %BF, FM, and FFM. ADP underestimated %BF compared to DXA and ANTH by 5.0% and 1.4%, respectively. Agreement between techniques was influenced by body fatness, height, age, and gender (all P < 0.05). Relatively good agreement was observed between ADP and both DXA and ANTH for FM and FFM. In conclusion, the underestimation of %BF by ADP compared to DXA may be of a magnitude that is clinically significant, especially when using %BF in children to confirm a diagnosis of obesity. Further development of body‐composition techniques for young children need to account for variability in age, gender, and level of fatness. Am. J. Hum. Biol. 18:470–480, 2006. © 2006 Wiley‐Liss, Inc.
Gene flow in the Iberian Peninsula determined from Y‐chromosome STR loci
Authors: Peña José A. , Garcia‐Obregon Susana , Perez‐Miranda Ana M. , De Pancorbo Marian M. , Alfonso‐Sanchez Miguel A. 
Published: June 2006

AbstractIn this work, seven multiallelic short‐tandem repeat (STR) loci from the nonrecombining region of the human Y‐chromosome (DYS19, DYS389 I, DYS389 II, DYS390, DYS391, DYS392, and DYS393) were typed in a sample of residents in the Basque Country (RBAS). In all, 40 different Y‐STR haplotypic combinations were identified, resulting in a value of haplotypic diversity of 0.979. Y‐STR data compiled from previous works were used for studying Y‐chromosome diversity in the Iberian Peninsula and for assessing the effects of migratory movements on the genetic background of the population living currently in territories traditionally occupied by native (autochthonous) Basques. An analysis of the spatial distribution of allelic frequencies of the Y‐STRs revealed a geographic pattern characterized by variation gradients (frequency clines) oriented for the most part in the direction southwest‐northeast. Accordingly, a neighbor‐joining analysis showed a relative polarization between populations located in the northeast and center of the Iberian Peninsula, and the rest of the samples considered. The study sample (RBAS) occupied an intermediate position in the population tree between the autochthonous Basques (BASQ) and the remaining samples. Interestingly, the RBAS collection only showed genetic heterogeneity with that of native Basques (ΦST = 0.013, P < 0.05). Estimates of admixture proportions in the gene pool of RBAS indicated a high level of hybridization with Basque (56%) and non‐Basque (44%) genes, which could explain the genetic differentiation observed between BASQ and RBAS. Am. J. Hum. Biol. 18:532–539, 2006. © 2006 Wiley‐Liss, Inc.
Common set of genes regulates low‐density lipoprotein size and obesity‐related factors in Alaskan Eskimos: Results from the GOCADAN Study
Authors: Voruganti V. Saroja , Cai Guowen , Cole Shelley A. , Freeland‐Graves Jeanne H. , Laston Sandra , Wenger Charlotte R. , MacCluer Jean W. , Dyke Bennett , Devereux Richard , Ebbesson Sven O.E. , Fabsitz Richard R. , Howard Barbara V. , Comuzzie Anthony G. 
Published: June 2006

AbstractIncreasing incidence of cardiovascular disease in traditionally low‐risk Alaskan Eskimos is a cause for concern. The purpose of this study was to examine the genetic and environmental correlations of low‐density lipoprotein (LDL) subfractions with obesity‐related factors in Alaskan Eskimos, using data from the first 954 participants of the Genetics of Coronary Artery Disease in Alaska Natives Study. Estimates of genetic and environmental influence were calculated using a maximum likelihood variance component method implemented in SOLAR. Mean values of weight, body mass index (BMI), and waist were 73.4 ± 0.5 kg, 27.6 ± 0.2 kg/m2, and 88.0 ± 0.4 cm, respectively. LDL, and its small (LDL1), medium (LDL2), and large (LDL3) subfractions, had mean values of 115.8 ± 1.2 mg/dl, 8.3 ± 0.4 mg/dl, 19.6 ± 0.8 mg/dl, and 71.5 ± 1.5 mg/dl, respectively. Bivariate analysis displayed significant genetic correlations between LDL subfractions and obesity‐related factors: LDL1 with BMI (rhoG = 0.67, P < 0.05), waist (rhoG = 0.80, P < 0.001), and subscapular and tricep skinfolds (rhoG = 0.93, P < 0.005, and rhoG = 0.78, P < 0.05, respectively); LDL2 with BMI (rhoG = 0.52, P < 0.05), waist (rhoG = 0.46, P < 0.05), and tricep skinfold (rhoG = 0.60, P < 0.05); and mean LDL size with BMI (rhoG = −0.36), waist (rhoG = −0.42,), and subscapular and tricep skinfolds (rhoG = −0.44 and −0.43, respectively) (P < 0.005). These results show that a common set of genes is influencing LDL size and obesity‐related factors in Alaskan Eskimos. Am. J. Hum. Biol. 18:525–531, 2006. © 2006 Wiley‐Liss, Inc.
Legendre polynomial kernel estimation of a density function with censored observations and an application to clinical trials
Authors: Berman Simeon M. 
Published: June 2006

AbstractLet f(x), x ∈ ℝM, M ≥ 1, be a density function on ℝM, and X1, …., Xn a sample of independent random vectors with this common density. For a rectangle B in ℝM, suppose that the X's are censored outside B, that is, the value Xk is observed only if Xk ∈ B. The restriction of f(x) to x ∈ B is clearly estimable by established methods on the basis of the censored observations. The purpose of this paper is to show how to extrapolate a particular estimator, based on the censored sample, from the rectangle B to a specified rectangle C containing B. The results are stated explicitly for M = 1, 2, and are directly extendible to M ≥ 3.For M = 2, the extrapolation from the rectangle B to the rectangle C is extended to the case where B and C are triangles. This is done by means of an elementary mapping of the positive quarter‐plane onto the strip {(u, v): 0 ≤ u ≤ 1, v > 0}. This particular extrapolation is applied to the estimation of the survival distribution based on censored observations in clinical trials. It represents a generalization of a method proposed in 2001 by the author [2].The extrapolator has the following form: For m ≥ 1 and n ≥ 1, let Km, n(x) be the classical kernel estimator of f(x), x ∈ B, based on the orthonormal Legendre polynomial kernel of degree m and a sample of n observed vectors censored outside B. The main result, stated in the cases M = 1, 2, is an explicit bound for E|Km, n(x) − f(x)| for x ∈ C, which represents the expected absolute error of extrapolation to C. It is shown that the extrapolator is a consistent estimator of f(x), x ∈ C, if f is sufficiently smooth and if m and n both tend to ∞ in a way that n increases sufficiently rapidly relative to m. © 2006 Wiley Periodicals, Inc.