bluuebunny/crossref_metadata_2025_split huggingface dataset:
Weak approximations for quantile processes of stationary sequences
Authors: Csörgö Miklós , Yu Hao 
Published: June 2006

AbstractABSTRACT By studying the deviations between uniform empirical and quantile processes (the so‐called Bahadur‐Kiefer representations) of a stationary sequence in properly weighted sup‐norm metrics, we find a general approach to obtaining weighted results for uniform quantile processes of stationary sequences. Consequently we are able to obtain weak convergence for weighted uniform quantile processes of stationary mixing and associated sequences. Further, by studying the sup‐norm distance of a general quantile process from its corresponding uniform quantile process, we find that information at the two end points of the uniform quantile process can be so utilized that this weighted sup‐norm distance converges in probability to zero under the so‐called Csörgõ‐Révész conditions. This enables us to obtain weak convergence for weighted general quantile processes of stationary mixing and associated sequences.
A comparison of tests for trend with historical controls in carcinogen bioassay
Authors: Fung Karen Y. , Krewski D. , Smythe R. T. 
Published: June 2006

AbstractABSTRACT Tests for trend in tumour response rates with increasing dose in long‐term laboratory studies of carcinogenicity that take into account historical control information are discussed. The theoretical basis for these tests is described, and their small‐sample properties evaluated using computer simulation. The performance of these tests is also evaluated using data from carcinogenicity experiments conducted under the U.S. National Toxicology Program. Based on these results, recommendations are made as to the most appropriate tests in practice. When the assumptions underlying these tests are satisfied, the use of historical control information is shown to result in an increase in power relative to the classical Cochran‐Armitage test that is widely used without historical controls.
When is a curve an outlier? An account of a tricky problem
Authors: Manchester Lise , Blanchard Wade 
Published: June 2006

AbstractABSTRACT The analysis of a set of data consisting ofNshort (≤20 observations each) multivariate time series, where the observations are irregularly spaced and where observations for the different components of each multivariate series are observed at different times, is discussed. With the increased use of automatic recording devices in many fields, data such as these, which are of course samples from smooth response curves, are becoming more common. In this application, which was a clinical trial comparing two cements for use in hip replacement surgery, the key to the analysis was in recognizing that the interest lay in the degree to which the five curves representing a patient's vital signs deviated from baseline (i.e., normal for that patient) during surgery. This enabled the statisticians to define appropriate response variables. The analysis included Rosseeuw's (1984) technique for the identification of multivariate outliers and logistic regressions to identify any effects on the process producing the outliers due to treatment or covariates.
The role of probability generating functions for estimation in incompletely observed random walks
Authors: Maki Ellen , Mcdunnough Philip 
Published: June 2006

AbstractPartial observation of a random walk results in independent convolutions of i.i.d. variables. It is known that under a scheme of sufficiently frequent observation, moments of the random walk can be consistently estimated. In these cases, probability generating functions (p.g.f.'s) can be used to circumvent the difficulties posed by likelihood estimation involving convolutions. Asymptotic properties of the p.g.f. estimates are given, and a comparison is made with the method‐of‐moments estimator, which is also shown to be asymptotically normal. In a parametric context, the p.g.f. estimator is shown to be asymptotically efficient. Monte Carlo experiments demonstrate that there are advantages to using the p.g.f.‐based estimate in small samples as well.
Some results on bootstrap prediction intervals
Authors: Mojirsheibani Majid , Tibshirani Robert 
Published: June 2006

AbstractWe investigate the construction of a BCa‐type bootstrap procedure for setting approximate prediction intervals for an efficient estimator θm of a scalar parameter θ, based on a future sample of size m. The results are also extended to nonparametric situations, which can be used to form bootstrap prediction intervals for a large class of statistics. These intervals are transformation‐respecting and range‐preserving. The asymptotic performance of our procedure is assessed by allowing both the past and future sample sizes to tend to infinity. The resulting intervals are then shown to be second‐order correct and second‐order accurate. These second‐order properties are established in terms of min(m, n), and not the past sample size n alone.
A parametric model for heterogeneity in paired Poisson counts
Authors: Goutis Constantinos , Galbraith Rex F. 
Published: June 2006

AbstractWe present a model for data in the form of matched pairs of counts. Our work is motivated by a problem in fission‐track analysis, where the determination of a crystal's age is based on the ratio of counts of spontaneous and induced tracks. It is often reasonable to assume that the counts follow a Poisson distribution, but typically they are overdispersed and there exists a positive correlation between the numbers of spontaneous and induced tracks in the same crystal. We propose a model that allows for both overdispersion and correlation by assuming that the mean densities follow a bivariate Wishart distribution. Our model is quite general, having the usual negative‐binomial and Poisson models as special cases. We propose a maximum‐likelihood estimation method based on a stochastic implementation of the EM algorithm, and we derive the asymptotic standard errors of the parameter estimates. We illustrate the method with a data set of fission‐track counts in matched areas of zircon crystals.
A note on the scale parameter of the dirichlet process
Authors: Walker Stephen G. , Mallick Bani K. 
Published: June 2006

AbstractThis paper gives an interpretation for the scale parameter of a Dirichlet process when the aim is to estimate a linear functional of an unknown probability distribution. We provide exact first and second posterior moments for such functionals under both informative and noninformative prior specifications. The noninformative case provides a normal approximation to the Bayesian bootstrap.
Box‐Cox transformed linear models: A parameter‐based asymptotic approach
Authors: Chen Gemai , Lockhart Richard A. 
Published: June 2006

AbstractA Box‐Cox transformed linear model usually has the formy(λ) = μ + β1x1+… + βpxp+ oe, wherey(λ) is the power transform ofy.Although widely used in practice, the Fisher information matrix for the unknown parameters and, in particular, its inverse have not been studied seriously in the literature. We obtain those two important matrices to put the Box‐Cox transformed linear model on a firmer ground. The question of how to make inference on β = (β1,…,βp)Twhen λ; is estimated from the data is then discussed for large but finite sample size by studying some parameter‐based asymptotics. Both unconditional and conditional inference are studied from the frequentist point of view.
Estimation of an exponential quantile under pitman's measure of closeness
Authors: Kourouklis S. 
Published: June 2006

AbstractWe consider the problem of estimating a quantile of an exponential distribution with unknown location and scale parameters under Pitman's measure of closeness (PMC). The loss function is required to satisfy some mild conditions but is otherwise arbitrary. An optimal estimator is obtained in the class of location‐scale‐equivariant estimators, and its admissibility in the sense of PMC is investigated.
Lower bounds on Bayes risks for estimating a normal variance: With applications
Authors: Vidakovic Brani , Dasgupta Anirban 
Published: June 2006

AbstractBrown and Gajek (1990) gave useful lower bounds on Bayes risks, which improve on earlier bounds by various authors. Many of these use the information inequality. For estimating a normal variance using the invariant quadratic loss and any arbitrary prior on the reciprocal of the variance that is a mixture of Gamma distributions, we obtain lower bounds on Bayes risks that are different from Borovkov‐Sakhanienko bounds. The main tool is convexity of appropriate functionals as opposed to the information inequality. The bounds are then applied to many specific examples, including the multi‐Bayesian setup (Zidek and his coauthors). Subsequent use of moment theory and geometry gives a number of new results on efficiency of estimates which are linear in the sufficient statistic. These results complement earlier results of Donoho, Liu and MacGibbon (1990), Johnstone and MacGibbon (1992) and Vidakovic and DasGupta (1994) for the location case.
Effects of the regulators paclobutrazol and flurprimidol on the growth of terminal sprouts formed on trimmed silver maple trees
Authors: Camacho Fernando , Arron Geoffrey 
Published: June 2006

AbstractApproximately 500,000 trees, growing near or under electrical distribution lines, are trimmed by Ontario Hydro each year at a cost of about $25 per tree. Several new growth regulators have been identified that are effective in controlling the growth of trimmed trees. Two such regulators are PP333 (paclobutrazol) and EL500 (flurprimidol). This paper presents the analysis of the data from an experiment conducted by Ontario hydro in which trimmed silver maple trees were trunk‐injected with different doses of these growth regulators. It was found that high doses of the growth regulators, particularly PP333 (20 g/L), were effective in retarding sprout growth.
In search of the “best” growth inhibitor
Authors: Lee Hyun Suk , Philips Bob 
Published: June 2006

AbstractIn searching for the “best” growth inhibitor, we decided to consider growth inhibition in terms of the lengths of the terminal sprouts. For it is logical to infer that the trees with the longer sprouts (after a 20‐month period) will most likely be the ones that will need trimming in the future. Additionally, we reasoned that if a particular treatment produced a smaller proportion of “long” sprouts, then it would be a more effective growth inhibitor. It was now necessary to define what was meant by “long”. After consultation with foresters we chose cutoff lengths of 15.0, 25.0 and 35.0 cm. Hence the response variable was chosen to be the proportion of the terminal sprouts on a tree that exceeded a specified cutoff length. By varying the cutoff lengths, we would minimize the effect of the arbitrariness involved in choosing one particular length.
Rank correlation methods for missing data
Authors: Alvo Mayer , Cabilio Paul 
Published: June 2006

AbstractThe subject of rank correlation has had a rich history. It has been used in numerous applications in tests for trend and for independence. However, little has been said about how to define rank correlation when the data are incomplete. The practice has often been to ignore missing observations and to define rank correlation for the smaller complete record. We propose a new class of measures of rank correlation which are based on a notion of distance between incomplete rankings. There is the potential for a significant increase in efficiency over the approach which ignores missing observations as demonstrated by a specific case.
A circular‐cone test for testing homogeneity against a simple tree order
Authors: Conaway Mark , Pillers Carolyn , Robertson Tim , Sconing Jim 
Published: June 2006

AbstractSeveral methods exist for the problem of testing the equality of several treatments against the one‐sided alternative that the treatments are better than the control. These methods include Dunnett's test, Bartholomew's likelihood‐ratio test, the Abelson‐Tukey‐Schaafsma‐Smid optimal‐contrast test, and the multiple‐contrast test of Mukerjee, Robertson, and Wright. A new test is proposed based on an approximation of the likelihood‐ratio test of Bartholomew. This test involves using a circular cone in place of the alternative‐hypothesis cone. The circular‐cone test has excellent power characteristics similar to those of Bartholomew's test. Moreover, it has the advantages of being simpler to compute and may be used with unequal sample sizes.
Selection of the splined variables and convergence rates in a partial spline model
Authors: Chen Hung , Chen Keh‐Wei 
Published: June 2006

AbstractA method based on the principle of unbiased risk estimation is used to select the splined variables in an exploratory partial spline model proposed by Wahba (1985). The probability of correct selection based on the proposed procedure is discussed under regularity conditions. Furthermore, the resulting estimate of the regression function achieves the optimal rates of convergence over a general class of smooth regression functions (Stone 1982) when its underlying smoothness condition is not known.
Modeling heteroscedastic age‐period‐cohort cancer data
Authors: MacNeill I. B. , Mao Y. , Xie L. 
Published: June 2006

AbstractThe extent to which cancer will be a burden on the Canadian health‐care system will be determined by future cancer rates and future population levels in the high‐risk age groups. Parametric models of incidence and mortality rates for various cancers may be used to obtain medium‐term forecasts of rates, which then can be used in conjunction with population projections to obtain forecasts of total incidence and mortality. Age‐period‐cohort cancer data often exhibit marked heteroscedasticity, which complicates the modeling of the data. Methods to allow for the effects of this heteroscedasticity on residual processes are developed and discussed in the context of modeling Canadian female breast‐cancer incidence data.
Inferential estimation, likelihood, and linear pivotals
Authors: Sprott D. A. 
Published: June 2006

AbstractA linear pivotal is introduced as a generalization of an estimate. The defining feature of a linear pivotal is that it generates a likelihood function in the same way as does a statistic. This allows the efficiency and sufficiency of the pivotal to be examined. The efficient reduction of data to a linear pivotal can often be achieved when it cannot be achieved using an estimate. Maximum‐likelihood estimation can be interpreted as a method of generating approximate sufficient linear pivotals.
Kurtosis and spread
Authors: Balanda Kevin P. , Macgillivray H. L. 
Published: June 2006

AbstractAn increase in kurtosis is achieved through the location‐ and scale‐free movement of probability mass from the “shoulders” of a distribution into its centre and tails. We introduce a coherent structure of ordering and measures, requiring no symmetry assumption, that represent different formalizations of this movement. For this purpose spread functions and spread‐spread plots are defined. The orderings impose growth patterns on the spread‐spread plot of the distributions involved, and the weakest involve both a specific scale‐matching technique and placement of “shoulders”. The role of existing kurtosis orderings and measures in this general context is identified and examples discussed throughout.
Tests for the mean of a multivariate normal population
Authors: Hudon Georges 
Published: June 2006

AbstractWe study the problem of testing: H0: μ ∈ P against H1: μ ∉ P, based on a random sample ofNobservations from ap‐dimensional normal distribution Np(μ, Σ) with Σ > 0 and P a closed convex positively homogeneous set. We develop the likelihood‐ratio test (LRT) for this problem. We show that the union‐intersection principle leads to a test equivalent to the LRT. It also gives a large class of tests which are shown to be admissible by Stein's theorem (1956). Finally, we give the α‐level cutoff points for the LRT.
Comparison of experiments for a class of positively dependent random variables
Authors: Shaked Moshe , Tong Y. L. 
Published: June 2006

AbstractUsing Blackwell's definition for comparison of experiments, it is shown that some sets of positively dependent random variables are less informative than similar sets of independent random variables. It is also shown that the information content of symmetric multivariate normal random vectors with a common known variance increases as the common correlation coefficient decreases. Some results which compare members of two‐parameter exponential families are also included.
The case of the random telephone numbers
Authors: Johnston Bruce 
Published: June 2006

AbstractAn unusual pattern of telephone numbers recorded on suspect credit‐card purchases led to the development of a generalized multinomial distribution. One usage of credit cards included 26 telephone numbers with a pattern of 7 unique numbers: 16 of one kind, 4 of another, 2 of another, and 4 different ones. With the assumption that the probability of any digit appearing in a telephone number is 1/10, the pattern of the 26 telephone numbers is found to have a probability of occurrence on the order of 1/1065. That is, the chance of such a pattern occurring at random is roughly equivalent to winning nine times in a row the popular ottery, LOTO 6/49.
Step‐function covariate effects in the proportional‐hazards model
Authors: Leblanc Michael , Crowley John 
Published: June 2006

AbstractWe describe some computationally efficient methods for building proportional‐hazards models with piecewise constant relative risk functions. The proposed techniques can be used to fit and assess a single step‐function (changepoint) term or as flexible exploratory survival‐data analysis tools. In addition, these tools can be used to include step‐function terms in more general proportional‐hazards models such as tree‐based models. An application to the development of prognostic groups based on data from a clinical trial for myeloma is presented.Nous décrivons quelques méthodes de calcul efficaces pour construire des modèles de risques proportionnels avec des functions de risque constantes par pièces. Les techniques proposés peuvent ětre utilisées pour ajuster et évaluer un terme fonction par étage/point de changement ou comme un outil d'analyse flexible d'exploration de données de survie. De plus, ces outils peuvent ětre utilisés pour inclure des termes fonctions par étages dans des modèles de risques proportionnels plus généraux tels que les modèles à base d'arbres. Une application au développement de groupes de prognostiques basée sur des données d'une enquète sur le myélome est présentée.
Estimating risk reduction in stein estimation
Authors: Venter J. H. , Steel S. J. 
Published: June 2006

AbstractIt is shown that the unbiased estimator of the risk reduction in Stein estimation is unsatisfactory from a mean‐squared‐error point of view. A truncated form of the unbiased estimator and various empirical Bayes estimators of the risk reduction are shown to perform much better than the unbiased estimator. A simple practical estimator is proposed whose performance is a compromise between that of the truncated and empirical Bayes estimators.
Moments of the sampled autocovariances and autocorrelations for a Gaussian white‐noise process
Authors: Anderson Oliver D. 
Published: June 2006

AbstractCumulants, moments about zero, and central moments are obtained for the mean‐corrected serial covariances and serial correlations for series realizations of lengthnfrom a white‐noise Gaussian process. All first and second moments (and some third, fourth, and higher moments) are given explicitly for the serial covariances; and the corresponding moments for the serial correlations are derived either explicitly or implicitly.
The limiting distribution of the least‐squares estimator in nearly integrated seasonal models
Authors: Perron Pierre 
Published: June 2006

AbstractWe consider the least‐squares estimator of the autoregressive parameter in a nearly integrated seasonal model. Building on the study by Chan (1989), who obtained the limiting distribution, we derive a closed‐form expression for the appropriate limiting joint moment generating function. We use this function to tabulate percentage points of the asymptotic distribution for various seasonal periods via numerical integration. The results are extended by deriving a stochastic asymptotic expansion to order Op(T‐l), whose percentage points are also obtained by numerically integrating the appropriate limiting joint moment generating function. The adequacy of the approximation to the finite‐sample distribution is discussed.
Laws of the iterated logarithm and large deviations for a class of diffusionl processes
Authors: Remillard Bruno , Dawson Donald A. 
Published: June 2006

AbstractWe use the martingale approach to study large deviations and laws of the iterated logarithm for certain multidimensional diffusion processes. The criteria for the validity of these properties are expressed in terms of averaging properties of the coefficients of the infinitesimal generator. In particular we apply our results to diffusion processes with random coefficients.
On the asymptotic distributions of high‐order spacings statistics
Authors: Guttorp P. , Lockhart R. A. 
Published: June 2006

AbstractGoodness‐of‐fit tests for the uniform distribution based on sums of smooth functions ofm‐spacings are studied. A limiting sum‐of‐weighted‐chi‐squareds approximation is shown to be accurate uniformly inmfor the special cases of analogues of Greenwooďs statistic and Moran's statistic. Asymptotic critical points are provided; theory and Monte Carlo studies show they are accurate for allmprovidednis moderately large.
A consistent nonparametric density estimator for the deconvolution problem
Authors: Liu Ming Chung , Taylor Robert L. 
Published: June 2006

AbstractThe problem of nonparametric estimation of a probability density function when the sample observations are contaminated with random noise is studied. A particular estimator f̌n(x) is proposed which uses kernel‐density and deconvolution techniques. The estimator f̌n(x) is shown to be uniformly consistent, and its appearance and properties are affected by constantsMnandhnwhich the user may choose. The optimal choices ofMnandhndepend on the sample sizen, the noise distribution, and the true distribution which is being estimated. Particular selections forMnandhnwhich minimize upper‐bound functions of the mean squared error for f̌n(x) are recommended.
Robust kernel estimators for additive models with dependent observations
Authors: Bianco Ana , Boente Graciela 
Published: June 2006

AbstractRobust nonparametric estimators for additive regression or autoregression models under an α‐mixing condition are proposed. They are based on local M‐estimators or local medians with kernel weights, and their asymptotic behaviour is studied. Moreover, diese local M‐estimators achieve the same univariate rate of convergence as their linear relatives.
Convergence de I estimateur a noyau de derivees de Radon‐Nikodym generales dans le cas melangeant
Authors: Bensaid Nadia , Fabre Jean‐Pierre 
Published: June 2006

AbstractIn many situations, nonparametric inference in point‐process theory consists in estimating a Radon‐Nikodym derivative of a nonnegative measure p with respect to another nonnegative measure v, where p and v are intensities of point processes. We consider the case of a mixing andstrictly stationary sequence of point processes and establish convergence results for the kernel estimator.
Normalité asymptotique d'estimateurs convergents du mode conditionnel
Authors: Berlinet Alain , Gannoun Ali , Matzner‐Løber Eric 
Published: June 2006

AbstractNous donnons des théorèmes généraux de normalité asymptotique des estimateurs convergents du mode conditionnel, indépendamment de la structure de dépendance des données et les appliquons au cas d'un processus stationnaire α‐mélangeant.