bluuebunny/crossref_metadata_2025_split huggingface dataset:
On the asymptotic efficiency of aligned‐rank tests in randomized block designs
Authors: Tardif Serge 
Published: June 2006

AbstractThis paper is concerned with the ranking‐after‐alignment procedure, the alignment being made on the mean, in randomized block designs. The asymptotic efficiencies, as the number of blocks goes to infinity, of a class of aligned‐rank tests, relative to the maximin most powerful test based on aligned observations, are established and studied. Some asymptotic efficiencies under doubleexponentiality are also obtained using Monte Carlo methods.
The asymptotic distribution of the correlation coefficient in testing fit to the exponential distribution
Authors: Lockhart R. A. 
Published: June 2006

AbstractThe asymptotic distribution of certain tests of fit to the exponential distribution is obtained. The tests are based on regression of the order statistics on their expectations under a standard exponential distribution. Asymptotic normality at the rate (logn)1/2is obtained for a family of statistics including the correlation coefficient.
Goodness‐of‐link testing in ordinal regression models
Authors: Genter Frederic C. , Farewell Vernon T. 
Published: June 2006

AbstractMcCullagh (1980) presented a comprehensive review of regression models for ordinal response variables. In these models, the functional relationship between the covariates and the response categories is dependent on thelink function. This paper shows that discrimination between links is feasible when the response variable is ordinal. Using the log‐gamma distribution of Prentice (1974), a generalized link function is constructed which allows discrimination between the probit, log‐log, and complementary log‐log links. Sample‐size considerations are noted, and examples are presented.
Comparison of two life distributions on the basis of their percentile residual life functions
Authors: Joe Harry , Proschan Frank 
Published: June 2006

AbstractLetFandGbe life distributions with respective failure rate functionsrFandrGand respective 100α‐percentile (0 < α < 1) residual life functionsqα,F, andqα, G. Distribution‐free two‐sample tests are proposed for testingH0:F=GagainstH1,α,:qα,F≥qα,GandH2 α:qβ,F≥qβ,Gfor all β ≥ α. This class of tests includes as a special case the test of Kochar (1981) for the alternativeH*2:rF≤rG. A theorem of Govindarajulu (1976) is extended in order to obtain asymptotic normality of the test statistics. The conditionqα,F≥qα,Gis implied byrF≤rGand is unrelated to the stochastic orderingF≤G; ifFandGare Weibull distributions with respective shape parametersc1andc2such that 1 ≤C1<C2, thenqα,F≥qα,Gfor all α larger than a function of the parameters.
Relative efficiencies of goodness‐of‐fit procedures with truncated data
Authors: Koziol James A. , John Petkau A. 
Published: June 2006

AbstractEfficiency properties of the Kolmogorov‐Smirnov, Kuiper, Cramér‐von Mises, and Anderson‐Darling statistics with truncated data are investigated. For these statistics, the approximate slopes are determined, and the equivalence of ratios of limiting approximate slopes to limiting Pitman efficiencies is established. Efficiencies for certain parametric families of distributions incorporating both location and scale alternatives are evaluated as the amount of truncation increases.
On the distribution of the correlation coefficient when sampling from a mixture of two bivariate normal densities: Robustness and the effect of outliers
Authors: Srivastava M. S. , Lee G. C. 
Published: June 2006

AbstractThe distribution of the sample correlation coefficient is derived when the population is a mixture of two bivariate normal distributions with zero mean but different covariances and mixing proportions 1 ‐ λ and λ respectively; λ will be called the proportion of contamination. The test of ρ = 0 based on Student'st, Fisher'sz, arcsine, or Ruben's transformation is shown numerically to be nonrobust when λ, the proportion of contamination, lies between 0.05 and 0.50 and the contaminated population has 9 times the variance of the standard (bivariate normal) population. These tests are also sensitive to the presence of outliers.
A reexamination of stein's antifiducial example
Authors: Plante AndrÉ 
Published: June 2006

AbstractIn Stein's 1959 example, for any sample withnsufficiently large, there is a confidence set embedded simultaneously within two regular confidence belts—one with coverage frequency smaller than an arbitrary positive ϵ, the other with coverage frequency larger than 1 — ϵ. Thus, Stein's example may be seen as an extreme case of mutually conflicting confidence statements, illustrating a possibility anticipated and denounced by Fisher.
Bayesian nonparametric survival analysis for grouped data
Authors: Johnson Wesley , Christensen Ronald 
Published: June 2006

AbstractA Bayesian nonparametric estimate of the survival distribution is derived under a particular sampling scheme for grouped data that includes the possibility of censoring. The estimate uses the prior information to smooth the data, giving an estimate which is continuous. As special cases survival estimates for life tables are obtained and the estimate of Susarla and Van Ryzin (1976) is derived. As the weight of the prior information tends to zero, the Bayesian estimate reduces to a continuous version of the nonparametric maximum‐likelihood estimate. An empirical Bayes modification of the procedure is illustrated on a data set from Cutler and Ederer (1958).
Optimal confidence regions in GMANOVA
Authors: Hooper Peter M. , Yau Wai Kwok 
Published: June 2006

AbstractInvariant confidence regions with smallest expected normalized volume are derived for a matrix of means of interest in the GMANOVA model, assuming only that the distribution of the matrix of residuals is left orthogonally invariant. Two invariance groups are considered, the usual full group and an amenable subgroup. A particular choice of the normalizing term leads to conditionally optimal confidence regions. Numerical results are given comparing the conditionally optimal fully invariant confidence region, which corresponds to the likelihood‐ratio test, with a conditionally minimax confidence region.
Approximate theory of multiway block designs
Authors: Pukelsheim Friedrich 
Published: June 2006

AbstractOptimality properties of multiway block designs are deduced from the general results of J. Kiefer's approximate‐design theory. In the model with additive effects these optimality properties solely depend on the two‐dimensional marginals of the designs. Uniform designs, and designs whose two‐dimensional marginals are products of the one‐dimensional marginals, are shown to be optimal. Approximate Youden designs are introduced for the case when the support sets of the two‐dimensional marginals are prescribed in advance. They are optimal in a relatively small class of competing designs only.
Approximations to noncentral distributions
Authors: Cox David R. , Reid Nancy 
Published: June 2006

AbstractTwo methods for approximating the distribution of a noncentral random variable by a central distribution in the same family are presented. The first consists of relating a stochastic expansion of a random variable to a corresponding asymptotic expansion for its distribution function. The second approximates the cumulant generating function and is used to provide central χ2and gamma approximations to the noncentral χ2and gamma distributions.
Residual components in generalized linear models
Authors: Brant Rollin 
Published: June 2006

AbstractThe adequacy of a postulated generalized linear model can often be improved by transforming predictors and/or including additional explanatory variables. To assess the fit relative to a given predictor, we define its corresponding residual component. Asymptotic bias and variance of the residual component are considered, paying particular attention to the case that the presumed model is valid.
Conditional and predictive probability integral transforms
Authors: O'Reilly Federico J. , Villegas Cesareo 
Published: June 2006

AbstractThe conditional probability integral transform, used in goodness‐of‐fit tests, is compared with its Bayesian counterpart, the predictive probability integral transform, and it is shown that under suitable conditions, in the presence of an adequate group structure, they yield the same transformed random variable, usually uniform on the unit interval.
Equivariant estimation of a normal mean using a normal concomitant variable for covariance adjustment
Authors: Berry J. Calvin 
Published: June 2006

AbstractEquivariant point estimators of one component of a bivariate normal mean vector are considered when the second component is known. Equivariant point estimators are characterized and compared in terms of their risk functions with respect to a normalized squared‐error loss function. Specific point estimators that dominate the usual estimator when the squared correlation coefficient is sufficiently large are provided.
Use of time‐series methods in the preparation of quarterly estimates of vital events
Authors: Roberts Georgia R. 
Published: June 2006

AbstractThis paper considers the problem of producing quarterly estimates of provincial and national vital events. Modern time‐series methods would appear to be a promising tool for the short‐range forecasts required for the birth, death, and marriage series. Considerations in the choice of a suitable model are described, and a possible model for the marriage data is illustrated.
The function space D([O, ∞)ρ, E)
Authors: Ivanoff B. Gail 
Published: June 2006

AbstractThe Skorokhod topology is extended to the function spaceD([0, ∞)ρ,E) of functions, from [0, ∞)ρ to a complete separable metric spaceE, which are “continuous from above with limits from below. Criteria for tightness are developed. The case in whichEis a product space is considered, and conditions under which tightness may be proven componentwise are given. Various applications are studied, including a multidimensional version of Donsker's Theorem, and a functional Central Limit Theorem for a multitype Poisson cluster process.
A new test for residual randomness in a class of dynamic autocorrelated econometric models
Authors: Kerward L. R. 
Published: June 2006

AbstractThis paper presents a new test statistic for dynamic or stochastic mis‐specification for the dynamic demand or dynamic adjustment class of economic models. The test statistic is based on residual autocorrelations, asymptotically X2and is suspected to be of low power. The test is illustrated with an example from recent econometric literature.
Estimation bayesienne des effets dans le modele a effets aleatoires de classification double avec interaction
Authors: Clement Bernard 
Published: June 2006

AbstractThe problem of estimating the effects in a balanced two‐way classification with interaction\documentclass{article}\pagestyle{empty}\begin{document}$i = 1, \ldots ,I;j = 1, \ldots ,J;k = 1, \ldots ,K$\end{document}using a random effect model is considered from a Bayesian view point. Posterior distributions ofri,cjandtijare obtained under the assumptions thatri,cj,tijandeijkare all independently drawn from normal distributions with zero meansand variances\documentclass{article}\pagestyle{empty}\begin{document}$\sigma _r^2 ,\sigma _c^2 ,\sigma _t^2 ,\sigma _e^2$\end{document}respectively. A non informative reference prior is adopted for\documentclass{article}\pagestyle{empty}\begin{document}$\mu ,\sigma _r^2 ,\sigma _c^2 ,\sigma _t^2 ,\sigma _e^2$\end{document}. Various features of thisposterior distribution are obtained. The same features of the psoterior distribution for a fixed effect model are also obtained. A numerical example is given.
Some computational problems related to multinomial trials
Authors: Neuts Marcel F. , Carson Charles C. 
Published: June 2006

AbstractEfficient numerical algorithms are developed to evaluate several probabilities related to multinomial trials.In the first part of the paper, the probability distribution of the number of trials until the alternatives j, j = 1,… m, have occurred at least ij times is computed. The multinomial trials involve the m alternatives l,…, m, with positive probabilities Pl‐Pm of occurrence.In the second part, several aspects of a multinomial subset selection problem, discussed by S. S. Gupta and K. Nagel, are investigated.
Estimation of the mean utilizing prior information
Authors: Mehta J. S. , Raghunandanan K. 
Published: June 2006

AbstractKatti [1] et Mehta et Srinivasan [2] ont considérés le probléme d'estimer la moyenne μ d'une population quand de l'information a priori sur μ est disponible. Dans [1], la supposition est que cette information a priori est sous forme d'un “estimé deviné”. Un estimateur à deux degres de deux échantillons est présenté. Quand à [2], un estimateur “shrinkage” basé sur un seul échantillon et une “valeur à priori” est considéré. En suivant les techniques présentées dans ces deux articles, nous suggérons des estimateurs du paramètre quand les données viennent d'une population Binomiale ou Poisson.
Excursion and meander in random walk
Authors: Csáki E. , Mohanty S. G. 
Published: June 2006

AbstractBernoulli bridge, excursion and meander are defined on the symmetric random walk similarly to Brownian bridge, excursion and meander (cf. Chung 1976). Distributions of certain characteristics defined on these Bernoulli processes, which are of a combinatorial nature, and their limits are obtained. Using weak convergence, these derivations give a verification of some of the earlier results on Brownian excursion and Brownian meander, as well as some new results.
Asymptotic properties of randomly indexed sequences of random variables
Authors: Csörgó M. , Rychlik Z. 
Published: June 2006

AbstractConditions are given for a randomly indexed sequence of random variables to converge weakly. The key concept employed is the so‐called generalized Anscombe condition. The results give a method of determining sequential stopping rules, which have the required accuracy of estimation of an unknown parameter in the case when the observations are not necessarily independent and identically distributed.
Factor analysis regression
Authors: Basilevsky Alexander 
Published: June 2006

AbstractThe paper describes two regression models—principal components and maximum‐likelihood factor analysis—which may be used when the stochastic predictor varibles are highly intereorrelated and/or contain measurement error. The two problems can occur jointly, for example in social‐survey data where the true (but unobserved) covariance matrix can be singular. Departure from singularity of the sample dispersion matrix is then due to measurement error. We first consider the more elementary principal components regression model, where it is shown that it can be derived as a special case of (i) canonical correlation, and (ii) restricted least squares. The second part consists of the more general maximum‐likelihood factor‐analysis regression model, which is derived from the generalized inverse of the product of two singular matrices. Also, it is proved that factor‐analysis regression can be considered as an instrumental variables estimator and therefore does not depend on whether factors have been “properly” identified in terms of substantive behaviour. Consequently the additional task of rotating factors to “simple structure” does not arise.
A robust biplot
Authors: Daigle Gaétan , Rivest Louis‐Paul 
Published: June 2006

AbstractThis paper introduces a robust biplot which is related to multivariateM‐estimates. Then × pdata matrix is first considered as a sample of sizenfrom somep‐variate population, and robustM‐estimates of the population location vector and scatter matrix are calculated. In the construction of the biplot, each row of the data matrix is assigned a weight determined in the preliminary robust estimation. In a robust biplot, one can plot the variables in order to represent characteristics of the robust variance‐covariance matrix: the length of the vector representing a variable is proportional to its robust standard deviation, while the cosine of the angle between two variables is approximately equal to their robust correlation. The proposed biplot also permits a meaningful representation of the variables in a robust principal‐component analysis. The discrepancies between least‐squares and robust biplots are illustrated in an example.
A zero‐one law for large order statistics
Authors: Wang H. , Tomkins R. J. 
Published: June 2006

AbstractFix r ≥ 1, and let {Mnr} be the rth largest of {X1,X2,…Xn}, where X1,X2,… is a sequence of i.i.d. random variables with distribution function F. It is proved that P[Mnr ≤ un i.o.] = 0 or 1 according as the series Σ∞n=3Fn(un)(log log n)r/n converges or diverges, for any real sequence {un} such that n{1 ‐F(un)} is nondecreasing and divergent. This generalizes a result of Bamdorff‐Nielsen (1961) in the case r = 1.
Approximation spline de la prevision d'un processus fonctionnel autorégressif d'ordre 1
Authors: Besse Philippe C. , Cardot Hervé 
Published: June 2006

AbstractABSTRACT We present a method to approximate and forecast, on an entire interval, a continuous‐time process. For this purpose, we use the modelization of ARH(l) processes, defined by Bosq (1991). We deal with the practical problem of the discretization of the observed trajectories and approximate them by means of spline functions. We show by simulations that for well‐chosen smoothing parameters, good prediction can be obtained in comparison with the “predictable” part of the process. Finally, we apply this model to forecast road traffic and compare it with a SARIMA model.
Distribution of the cross‐correlations of squared residuals in ARIMA models
Authors: Wong H. , Li W. K. 
Published: June 2006

AbstractABSTRACT The distribution of the cross‐correlations of squared residuals from Box‐Jenkins models is considered in very general conditions, and the asymptotic distribution is derived. A test for a lagged relationship in volatility for economic time series under instantaneous causality is proposed, and its empirical behaviour is studied. An example involving the international stock market's volatility is studied, and an interesting result is observed.
Model influence functions based on mixtures
Authors: Gustafson Paul 
Published: June 2006

AbstractInfluence functions are considered as diagnostics for model departures in parametric Bayesian inference. A baseline model density is expressed as a mixture; then the mixing distribution is perturbed. This is designed to engender perturbations which are plausiblea priori.The influence of perturbations is measured for both Bayes estimates and their associated posterior expected losses. To assess the plausibility of perturbationsa posteriori, an additional influence function is constructed for the Bayes factor comparing the perturbed and baseline models. The effect of perturbation on various estimands is incorporated in the analysis.
Nonparametric regression estimation at design poles and zeros
Authors: Hengartner Nicolas W. , Linton Oliver B. 
Published: June 2006

AbstractABSTRACT In most treatments of nonparametric regression, it is assumed that the marginal density of the explanatory variables is strictly bounded away from zero and infinity. This note investigates the pointwise asymptotics for nonparametric regression when this assumption fails, that is, the marginal density of the explanatory variable has either an isolated zero or a pole at the point of interest.
Bayesian semiparametric inference for the accelerated failure‐time model
Authors: Kuo Lynn , Mallick Bani 
Published: June 2006

AbstractBayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coefficients and a mixture‐of‐Dirichlet‐processes prior on the unknown error distribution. A Markov‐chain Monte Carlo (MCMC) method is developed to compute the features of the posterior distribution. A model selection method for obtaining a more parsimonious set of predictors is studied. The method adds indicator variables to the regression equation. The set of indicator variables represents all the possible subsets to be considered. A MCMC method is developed to search stochastically for the best subset. These procedures are applied to two examples, one with censored data.
Line transects of two‐dimensional random fields: Estimation and design
Authors: Heckman Nancy , Rice John 
Published: June 2006

AbstractIn line‐transect sampling data are collected continuously along the path travelled. These data are then used to estimate some characteristic of an entire region. We model the characteristic of the region as a linear functional ofZ(), a realization of a random field, and our data as the values ofZ(x) for x along the path. We study how to estimate the characteristic of interest and how to choose the path to accurately estimate it.
Applications of bivariate and univariate local lyapunov exponents
Authors: Seifu Yodit , Reid N. 
Published: June 2006

AbstractChaotic systems are characterized by sensitivity to initial conditions, and this property can be measured by global Lyapunov exponents, which are measures of the average divergence rate of initially close trajectories. Wolff (1992) introduced local Lyapunov exponents and used them to obtain two diagnostic plots for differentiating between stochastic and deterministic time series. We extend the definition of the local Lyapunov exponent and the diagnostic plots to accommodate time series that arise from bivariate maps and investigate the behaviour of the local Lyapunov exponents and the corresponding diagnostic plots for some dynamical systems and stochastic time series. We consider the application of these diagnostic plots to some heart rate variability data.
Dataset on silver‐maple‐tree growth regulators
Authors: Sloan Jef F. A. , Schwarz Carl J. , Neden Linda R. 
Published: June 2006

AbstractA trivariate summary measure based upon the number of sprouts, the lengths of the sprouts, and the number of intemodes per sprout was developed to compare the effects of growth suppressants on tree regrowth after trimming. A new graphic, the tree picture, was developed to display this information. The tree picture is easy to interpret and revealed clear results for the data given. These results demonstrate how innovative graphical methodology can be applied as a summary device in the absence of or as a supplement to a statistical testing procedure.
Conditional inference for subject‐specific and marginal agreement: Two families of agreement measures
Authors: Cook Richard J. , Farewell Vern T. 
Published: June 2006

AbstractA two‐stage procedure is described for assessing subject‐specific and marginal agreement for data from a test‐retest reliability study of a binary classification procedure. Subject‐specific agreement is parametrized through the log odds ratio, while marginal agreement is reflected by the log ratio of the off‐diagonal Poisson means. A family of agreement measures in the interval [‐1, 1] is presented for both types of agreement. The conditioning argument described facilitates exact inference. The proposed methodology is demonstrated by way of an example involving hypothetical data chosen for illustrative purposes, and data from a National Health Survey Study (Rogot and Goldberg 1966).