bluuebunny/crossref_metadata_2025_split huggingface dataset:
A generalized step‐up‐down multiple test procedure
Authors: Tamhane Ajit C. , Liu Wei , Dunnett Charles W. 
Published: June 2006

AbstractA generalization of step‐up and step‐down multiple test procedures is proposed. This step‐up‐down procedure is useful when the objective is to reject a specified minimum number, q, out of a family of k hypotheses. If this basic objective is met at the first step, then it proceeds in a step‐down manner to see if more than q hypotheses can be rejected. Otherwise it proceeds in a step‐up manner to see if some number less than q hypotheses can be rejected. The usual step‐down procedure is the special case where q = 1, and the usual step‐up procedure is the special case where q = k. Analytical and numerical comparisons between the powers of the step‐up‐down procedures with different choices of q are made to see how these powers depend on the actual number of false hypotheses. Examples of application include comparing the efficacy of a treatment to a control for multiple endpoints and testing the sensitivity of a clinical trial for comparing the efficacy of a new treatment with a set of standard treatments.
Estimation of product moments of a stationary stochastic process with application to estimation of cumulants and cumulant spectral densities
Authors: Kim Peter T. 
Published: June 2006

AbstractInference concerning the structure of stationary stochastic processes can be investigated by looking at properties of various cumulant spectral densities of order two and higher. However, except for cases when cumulants and product moments are identical, estimation of higher‐order cumulant spectral densities has been restricted by the dependence of higher‐order cumulants on lower‐order product moments. By first estimating product moments and then using an identity between product moments and cumulants, asymptotically unbiased and consistent estimates of cumulants are obtained. This in turn leads to asymptotically unbiased and consistent estimators of higher‐order cumulant spectral densities. In addition, asymptotic normality of product‐moment estimators is exhibited under weak dependence.
Density estimation under the koziol‐green model of censoring by penalized likelihood methods
Authors: Gijbels I. , Klonias V. K. 
Published: June 2006

AbstractWe propose two density estimators of the survival distribution in the setting of the Koziol‐Green random‐censoring model. The estimators are obtained by maximum‐penalized‐likelihood methods, and we provide an algorithm for their numerical evaluation. We establish the strong consistency of the estimators in the Hellinger metric, the Lp‐norms, p= 1,2, ∞, and a Sobolev norm, under mild conditions on the underlying survival density and the censoring distribution.
Correlated linear models for the analysis of familial correlations
Authors: Shoukri M. M. , Mian I. U. H. , Tracy D. S. 
Published: June 2006

AbstractThe problems of estimation and hypotheses testing on the parameters of two correlated linear models are discussed. Such models are known to have direct applications in epidemiologic research, particularly in the field of family studies. When the data are unbalanced, the maximum‐likelihood estimation of the parameters is achieved by adopting a fairly simple numerical algorithm. The asymptotic variances and covariances of the estimators are derived, and the procedures are illustrated on arterial‐blood‐pressure data from the literature.
The analysis of incomplete data using stochastic covariates
Authors: Rai S.N. , Matthews D.E. 
Published: June 2006

AbstractIn the development of many diseases there are often associated random variables which continuously reflect the progress of a subject towards the final expression of the disease (failure). At any given time these processes, which we call stochastic covariates, may provide information about the current hazard and the remaining time to failure. Likewise, in situations when the specific times of key prior events are not known, such as the time of onset of an occult tumour or the time of infection with HIV‐1, it may be possible to identify a stochastic covariate which reveals, indirectly, when the event of interest occurred. The analysis of carcinogenicity trials which involve occult tumours is usually based on the time of death or sacrifice and an indicator of tumour presence for each animal in the experiment. However, the size of an occult tumour observed at the endpoint represents data concerning tumour development which may convey additional information concerning both the tumour incidence rate and the rate of death to which tumour‐bearing animals are subject. We develop a stochastic model for tumour growth and suggest different ways in which the effect of this growth on the hazard of failure might be modelled. Using a combined model for tumour growth and additive competing risks of death, we show that if this tumour size information is used, assumptions concerning tumour lethality, the context of observation or multiple sacrifice times are no longer necessary in order to estimate the tumour incidence rate. Parametric estimation based on the method of maximum likelihood is outlined and is applied to simulated data from the combined model. The results of this limited study confirm that use of the stochastic covariate tumour size results in more precise estimation of the incidence rate for occult tumours.
Beer chemistry and canadians' beer preferences
Authors: Whitmore G. A. , Gentleman Jane F. 
Published: June 2006

AbstractBeer companies want to understand the relationship between the chemical characteristics of beer and the preferences for beer exhibited by consumers. Two data sets were provided to the analysts in this case study. The first set consisted of chemical measurements on 91 beers and preference measurements on the same beers collected from beer consumers in blind taste tests. The analysts were asked to use these data to develop a statistical model relating beer chemistry and consumer preferences for beer. The second data set consisted of chemical measurements on a holdout sample of 37 beers. The analysts were asked to employ their statistical model to predict consumer preferences for the beers in the holdout sample. The case study assesses the success of their modelling efforts.
Assessing case influence on confidence intervals in nonlinear regression
Authors: Dzieciolowski Krzysztof , Ross William H. 
Published: June 2006

AbstractDiagnostic techniques are proposed for assessing the influence of individual cases on confidence intervals in nonlinear regression. The technique proposed uses the method of profilet‐plots applied to the case‐deletion model. The effect of the geometry of the statistical model on the influence measures is assessed, and an algorithm for computing case‐deleted confidence intervals is described. This algorithm provides a direct method for constructing a simple diagnostic measure based on the ratio of the lengths of confidence intervals. The generalization of these methods to multiresponse models is discussed.
Hierarchical bayesian curve fitting and smoothing
Authors: Angers Jean‐François , Delampady Mohan 
Published: June 2006

AbstractEstimation of a smooth function is considered when observations on this function added with Gaussian errors are observed. The problem is formulated as a general linear model, and a hierarchical Bayesian approach is then used to study it. Credible bands are also developed for the function. Sensitivity analysis is conducted to determine the influence of the choice of priors on hyperparameters. Finally, the methodology is illustrated using real and simulated examples where it is compared with classical cubic splines. It is also shown that our approach provides a Bayesian solution to some problems in discrete time series.
Orthogonalizing parametric link transformation families in binary regression analysis
Authors: Czado Claudia , Santner Thomas J. 
Published: June 2006

AbstractThis paper studies the application of the orthogonalization technique of Cox and Reid (1987) to parametric families of link functions used in binary regression analysis. The explicit form of Cox and Reid's condition (4), for orthogonality at a point, is derived for arbitrary link families. This condition is used to determine a transform of a family introduced by Burr (1942) and Prentice (1975, 1976) which is locally orthogonal when the regression parameter is zero. Thus the benefits of having orthogonal parameters are limited to “small” regression effects. The extent to which approximate orthogonality holds for nonzero regression coefficients is investigated for two data sets from the literature. Two specific issues considered are: (1) the ability of orthogonal reparametrization to reduce the variability of the regression parameters caused by estimation of the link parameter and (2) the improved numerical stability (and hence interpretability) of regression estimates corresponding to different link parameters.
Limit theorems for a general weighted process under random censoring
Authors: Einmahl John H.J. , Koning Alex J. 
Published: June 2006

AbstractNecessary and sufficient conditions for weak and strong convergence are derived for the weighted version of a general process under random censoring. To be more explicit, this means that for this process complete analogues are obtained of the Chibisov‐O'Reilly theorem, the Lai‐Wellner Glivenko‐Cantelli theorem, and the James law of the iterated logarithm for the empirical process. The process contains as special cases the so‐called basic martingale, the empirical cumulative hazard process, and the product‐limit process. As a tool we derive a Kiefer‐process‐type approximation of our process, which may be of independent interest.
Time series, point processes, and hybrids
Authors: Brillinger David R. 
Published: June 2006

AbstractTechniques developed for the study of time series, point processes, and marked point processes can suggest corresponding techniques for each other, and common techniques can be recognized. In this paper connections are drawn based on conceptual foundations, basic parameters, analyses, displays, algorithms, problems, models. The definitions and techniques are brought out by specific scientific problems. The emphasis is on the single‐realization stationary case and on the use of second‐ and third‐order moments to help understand the realization. The tool of stacking, at a particular period, is employed in several of the examples.
Binary‐image restoration
Authors: Meloche J. , Zamar R.H. 
Published: June 2006

AbstractWe consider the problem of binary‐image restoration. The image being restored is not random, and we make no assumption about the nature of its contents. The estimate of the colour at each site is a fixed (the same for all sites) function of the data available in a neighbourhood of that site. Under this restriction, the estimate minimizing the overall mean squared error of prediction is the conditional expectation of the true colour given the observations in the neighbourhood of a site. The computation of this conditional expectation leads to the formal definition of the local characteristics of an image, namely, the frequency with which each pattern appears in the true unobserved image. When the “true” distribution of the patterns is unknown, it can be estimated from the records. The conditional expectation described above can then be evaluated using the estimated distribution of the patterns, and this procedure leads to a very natural estimate of the colour at each site. We propose two unbiased and consistent estimates for the distribution of patterns when the noise is a Gaussian white noise. Since the size of realistic images is very large, the estimated pattern distribution is usually close to the true one. This suggests that the estimated conditional expectation can be expected to be nearly optimal. An interesting feature of the proposed restoration methods is that they do not require prior knowledge of the local or global properties of the true underlying image. Several examples based on synthetic images show that the new methods perform fairly well for a variety of images with different degrees of colour continuity or textures.
Nonparametric Bayes methods for directional data
Authors: Brunner Lawrence J. , Lo Albert Y. 
Published: June 2006

AbstractA model for directional data in q dimensions is studied. The data are assumed to arise from a distribution with a density on a sphere of q — 1 dimensions. The density is unimodal and rotationally symmetric, but otherwise of unknown form. The posterior distribution of the unknown mode (mean direction) is derived, and small‐sample posterior inference is discussed. The posterior mean of the density is also given. A numerical method for evaluating posterior quantities based on sampling a Markov chain is introduced. This method is generally applicable to problems involving unknown monotone functions.
Graphical methods for ranking data
Authors: Alvo Mayer , Ertas Kadir 
Published: June 2006

AbstractGraphical methods are presented for the analysis of ranking data collected from g groups of rankers. The data provided by a single individual consist of the ranks of r objects. The sample space is the space of all permutations and has cardinality r! In order to reduce the dimensionality of the data and to study the interrelationships among rankers and items, a two‐stage approach is proposed. First, transformations motivated by various metrics on permutations are defined. In particular, the Kendall metric gives rise to pairwise comparisons. Then, the transformed data are analyzed using results in connection with the generalized singular‐value decomposition of a matrix. The methods are illustrated on two examples.
Likelihood and higher‐order approximations to tail areas: A review and annotated bibliography
Authors: Reid N. 
Published: June 2006

AbstractRecent developments in higher‐order asymptotic theory for statistical inference have emphasized the fundamental role of the likelihood function in providing accurate approximations to cumulative distribution functions. This paper summarizes the main results, with an emphasis on classes of problems for which relatively easily implemented solutions exist. A survey of the literature indicates the large number of problems solved and solvable by this method. Generalizations and extensions, with suggestions for further development, are considered.
Estimation of regression parameters in generalized linear models for cluster correlated data with measurement error
Authors: Sutradhar B.C. , Rao J.N.K. 
Published: June 2006

AbstractLiang and Zeger (1986) introduced a class of estimating equations that gives consistent estimates of regression parameters and of their asymptotic variances in the class of generalized linear models for cluster correlated data. When the independent variables or covariates in such models are subject to measurement errors, the parameter estimates obtained from these estimating equations are no longer consistent. To correct for the effect of measurement errors, an estimator with smaller asymptotic bias is constructed along the lines of Stefanski (1985), assuming that the measurement error variance is either known or estimable. The asymptotic distribution of the bias‐corrected estimator and a consistent estimator of its asymptotic variance are also given. The special case of a binary logistic regression model is studied in detail. For this case, methods based on conditional scores and quasilikelihood are also extended to cluster correlated data. Results of a small simulation study on the performance of the proposed estimators and associated tests of hypotheses are reported.
Nonhomogeneous poisson processes as overhaul models
Authors: Dykstra R.L. , Barmi Hammou El , Guffey James M. , Wright F.T. 
Published: June 2006

AbstractHollander and Proschan (1974) studied nonhomogeneous Poisson processes as models for systems subject to overhauls. They did not postulate a functional form for the intensity, but showed that certain basic assumptions about the deterioration of the system implied that the mean function is superadditive. They studied tests of the null hypothesis that the intensity is constant with the alternative restricted to superadditive mean functions. For estimation purposes, the class of superadditive mean functions is too broad. We assume that the intensity is nondecreasing between overhauls and that at an overhaul it does not fall below its average prior to the overhaul. These two assumptions imply that the mean function is star‐shaped. We obtain the restricted maximum‐likelihood estimates under these two assumptions and under the star‐shaped restriction. The two estimates are compared on a data set.
Characterization theorems for some discrete distributions based on conditional structure
Authors: Wang Y. , Gupta A.K. , Nguyen T.T. 
Published: June 2006

AbstractThe joint distribution of (X,Y) is determined if the conditional expectationE{g(X)|Y=y} is given and the conditional distribution ofY|(X=x) is a conditional power series distribution, whereg(·) is a function satisfying some minor conditions.
Higher‐order asymptotics under model misspecification
Authors: Viraswami K. , Reid N. 
Published: June 2006

AbstractMost of the higher‐order asymptotic results in statistical inference available in the literature assume model correctness. The aim of this paper is to develop higher‐order results under model misspecification. The density functions to O(n−3/2) of the robust score test statistic and the robust Wald test statistic are derived under the null hypothesis, for the scalar as well as the multiparameter case. Alternate statistics which are robust to O(n−3/2) are also proposed.
A geometric optimally of Cox's partial likelihood
Authors: Chan Schultz , Ghosh Malay 
Published: June 2006

AbstractThe paper proves geometric optimality of Cox's partial likelihood score functions via estimating functions. As an illustration, Cox's proportional‐hazards model is considered.Les auteurs démontrent l'optimalité gǵométrique des fonctions scores de vraisemblance partielle de Cox au moyen des fonctions d'estimation. Le modéle des risques proportionnels de Cox permet d'illustrer leur résultat.
An invariance principle for triangular arrays of dependent variables with application to autocovariance estimation
Authors: Chen Hui , Romano Joseph P. 
Published: June 2006

AbstractThe invariance principle for triangular arrays of dependent variables is studied. We use the concept of mixingale, proposed by McLeish (1975). Uniform bounds are imposed on the growth of conditional expectations with respect to distant predecessors. The theorem is applied to invariance principles for autocovariance estimates based on triangular arrays of time‐series data for weak mixing sequences and linear processes. Such results are required for bootstrap applications.
The optimal prediction of cross‐sectional proportions in categorical panel‐data analysis
Authors: Zhang Ping 
Published: June 2006

AbstractLongitudinal study is a powerful tool in many areas of empirical research, including healthcare research, environmental monitoring and econometrics. In econometrics, the data generated through longitudinal studies are often referred to as panel data. Such data combine the features of both cross‐sectional and time‐series measurements. However, the vast literature on panel‐data analysis focuses almost exclusively on the treatment of cross‐sectional heterogeneity. Few studies address the problem of modeling panel data from a prediction point of view. In this article, we first formulate the prediction problem for categorical panel data. We then argue that prediction methods that are natural for other types of data may not be appropriate for panel data. Our main result shows that the optimal predictor, among a broad class of consistent predictors, is equivalent to a nonrandomized classification procedure that is determined by a set of integral equations.Le recours é des enquětes longitudinales est fréquent et souvent trés profitable dans des disciplines expérimentales comme les sciences de la santé, les sciences de l'environnement ou I'économétrie. Dans ce dernier domaine, ce type d'enquěte donne lieu á des données dites de panel, qui component á la fois une composante transversale et une composante chronologique. Les nombreux écrits sur le sujet concernent pour la plupart le traitement de l'hétérogénéité transversale. Rares sont les études qui s'attaquent au probléme de la modélisation et de la prévision de ce genre de données. L'auteur commence done par formuler le probléme de la prévision á partir de données de panel catégoriques. II soutient ensuite que certaines méthodes de prévision qui sont naturelles pour d'autres types de données ne conviennent pas forcément dans ce contexte. Son résultat principal est á l'effet que I'élemént qui est optimal au sein d'une large classe de prédicteurs convergents est en fait équivalent á une procédure de classification non aléatoire qui est solution d'un ensemble d'équations intégrates.
Order‐restricted hypothesis testing in a variation of the normal mixture model
Authors: Nettleton Dan 
Published: June 2006

AbstractThis paper examines likelihood‐ratio tests concerning the relationships among a fixed number of univariate normal means given a sample of normal observations whose population membership is uncertain. The asymptotic null distributions of likelihood‐ratio test statistics are derived for a class of tests including hypotheses which place linear inequality constraints on the normal means. The use of such tests in the interval mapping of quantitative trait loci is addressed.
Confidence estimation of a normal mean vector with incomplete data
Authors: Krishnamoorthy K. , Pannala Maruthy K. 
Published: June 2006

AbstractThe problem of confidence estimation of a normal mean vector when data on different subsets of response variables are missing is considered. A simple approximate confidence region is proposed when the data matrix is of monotone pattern. Simultaneous inferential procedures based on Scheffe's method and Bonferroni's method are outlined. Further, applications of the results to a repeated measurements model are given. The results are illustrated using a practical example.
Joint estimation of the mean and dispersion parameters in the analysis of proportions: A comparison of efficiency and bias
Authors: Paul S. R. , Islam A. S. 
Published: June 2006

AbstractThis paper deals with joint estimation of the mean and dispersion parameters in the analysis of proportions. We consider a parametric model, namely the extended beta‐binomial model, and several semiparametric procedures. We study large‐sample efficiency and small‐sample bias and efficiency properties of the estimates of the mean and intraclass correlation parameters. Estimation and efficiency calculations are présentés for the regression model. However, for simplicity, numerical large‐sample efficiency and small‐sample bias and efficiency calculations are performed for the two‐parameter model only. Numerical efficiency results are présentés in terms of graphs. Estimated asymptotic efficiencies of various estimates are also compared for two data sets. Our findings suggest that for the estimation of the mean (regression) parameters the quasilikelihood procedure performs best. However, for the joint estimation, the Gaussian likelihood estimates perform best.
Hidden Markov chains in generalized linear models
Authors: Turner T. Rolf , Cameron Murray A. , Thomson Peter J. 
Published: June 2006

AbstractWe show how the concept of hidden Markov model may be accommodated in a setting involving multiple sequences of observations. The resulting class of models allows for both interrelationships between different sequences and serial dependence within sequences. Missing values in the observation sequences may be handled in a straightforward manner. We also examine a group of methods, based upon the observed Fisher Information matrix, for estimating the covariance matrix of the parameter estimates. We illustrate the methods with both real and simulated data sets.