bluuebunny/crossref_metadata_2025_split huggingface dataset:
Resolution of Godambe's paradox
Authors: Genest Christian , Schervish Mark J. 
Published: June 2006

AbstractTwo versions of a statistical paradox involving an ancillarity principle have recently been proposed by Godambe (1979, 1982). In this note, we point out that the two versions of his paradox are actually quite different, and we resolve both of them by investigating their relationship to randomization. The first version of Godambe's paradox was criticized earlier by Good (1980), but we believe his argument to be flawed.
The block replacement model with inactivity periods and general cost structure
Authors: Tilquin C. , Cléroux R. 
Published: June 2006

AbstractThere exists a gap between the state of the art in this part of reliability theory devoted to preventive replacement and its applications to real world situations. This dichotomy results in part from the fact that standard models are too simple from the point of view of the cost structure to adequately deal with reality. In considering a general cost structure for the block replacement model with inactivity periods, we hope to contribute to filling this gap.
On indirect least squares estimation of a simultaneous equation system
Authors: Park Soo‐Bin 
Published: June 2006

AbstractThe indirect least squares (ILS) approach is broadly regarded as a procedure of deriving the structural parameters of a simultaneous equation system indirectly from the estimates of reduced‐form parameters. In this paper it is shown that the ILS approach yields estimators which are identical with the two‐stage least squares estimator, when applied to a single equation, and the three‐stage least squares estimator, when applied to the complete system. Thus a fresh motivation for the ILS method is provided.
Simulation de la fécondité au moyen d'un modèle basé sur la courbe du type I de pearson
Authors: Mitra S. , Romaniuk A. 
Published: June 2006

AbstractThere have been various attempts to use the Pearson type I curve for the purpose of graduating age‐specific fertility rates for human populations. This article attempts to broaden the scope of application of this curve, and discusses three particular areas where such application of this curve would be feasible. These areas are: simulation of age‐specific fertility rates; estimation of certain fertility parameters where statistics are either unreliable or incomplete as is the case in most developing countries; and projections of births. The writers feel that the greatest potential of the Pearsonian type I curve lies in the area of parametric model building for birth projections. One of the important prerequisites for a fruitful application in this area is simplification of procedures and reduction of a number of parameters needed to derive the Pearsonian type I curve. This article presents new methods for deriving this latter curve. The results of the proposed models are tested against data on age‐specific fertility rates for Canada.
A note on estimating the variance of the sample mean in stratified sampling
Authors: Dumouchel W. H. , Rothman E. , Govindarajulu Z. 
Published: June 2006

AbstractConsider a sample of n observations such that a single observation is randomly drawn from each of n strata, each having mean ui and variance σ2i (i=1,…, n). Two estimators for the variance of the sample mean are proposed and their relative efficiency is examined.
Analyzing point processes subjected to random deletions
Authors: Brillinger David R. 
Published: June 2006

AbstractCertain nonstationary point process data are viewed as having arisen through time dependent random deletions of a stationary point process. Initially the probability Of deletion is assumed known and estimates of the rate and autointensity function of the inherent stationary process are constructed. Then an estimate of the deletion probability function is developed for the case of the function depending on a finite dimensional parameter. An estimate is provided for the variance of the autointensity estimate.
On a cramér‐von mises type statistic for testing bivariate independence
Authors: Koziol James A. , Nemec Amanda F. 
Published: June 2006

AbstractA Cramér‐von Mises type statistic for testing bivariate independence, proposed by Hoeffding (1948) and by Blum, Kiefer, and Rosenblatt (1961), is examined in greater detail. The statistic is decomposed into components in the manner of Durbin and Knott (1972), and the components are shown to be related to linear rank statistics. Asymptotic power properties of the Hoeffding statistic and its components in testing for independence with bivariate normal random observations are described; a Monte Carlo study comparing these statistics with other nonparametric statistics for bivariate independence is also reported.
Goodness‐of‐fit tests of hypothesized canonical variables
Authors: Gupta R. D. , Kabe D. G. 
Published: June 2006

AbstractIn the literature goodness‐of‐fit tests for canonical variables are available either in thexspace or in theyspace, see e.g., Bartlett (1951), Kshirsagar (1971, 1972), Radcliffe (1968), and Williams (1952). Here we present goodness‐of‐fit tests for canonical variables in both thexandyspaces. The results appear as extensions of the results of the above authors.
Coupling methods in approximations
Authors: Wang Y. H. 
Published: June 2006

AbstractLet X and Y be two arbitrary k‐dimensional discrete random vectors, for k ≥ 1. We prove that there exists a coupling method which minimizes P(X ≠ Y). This result is used to find the least upper bound for the metric d(X, Y) = supA|P(X ∈ A) − P(Y ∈ A)| and to derive the inequality d(Σ Xi, Σ Yi) ≤ Σd(Xi, Yi). We thus obtain a unified method to measure the disparity between the distributions of sums of independent random vectors. Several examples are given.
Robustness of theil's mixed regression estimators
Authors: Swamy P.A.V.B. , Mehta J.S. 
Published: June 2006

AbstractAn efficient method for incorporating incomplete prior information in regression analysis was developed by Theil [1963]. In this paper we take up the estimator of coefficients given by this procedure and study its robustness to departures from normality of prior estimators of coefficients. The use of incomplete or biased prior information in regression analysis is also considered and a new estimator for the regression coefficient is suggested.
The correlated random walk in continuous time with barriers
Authors: Kapadia Asha Seth 
Published: June 2006

AbstractIn this paper correlation has been introduced between two successive transitions in the model investigated by Moyal and Heathcote (1959). The transition rate has been taken to be constant and for simplicity of mathematical expressions the time scale has been so chosen that this rate is unity. For the unrestricted walk we find that the mean position of the particle at time t is that position for which the probability of the particle arriving at some earlier instant from the left is 1/2. With both the barriers either absorbing or reflecting, we have obtained the Laplace Transform of the distributions, but because of the complicated nature of the mathematical expressions involved we are able to find only the asymptotic distributions. For the reflecting barrier case, we find that the process tends to a stationary distribution and that the expected position is eventually the middle point between the barriers.
Contribution empirique au probleme d'agregation de fonctions autoregressives: Le cas de la politique de distribution de dividendes
Authors: Chateau Jean‐Pierre D. 
Published: June 2006

AbstractThis paper deals with the aggregation problem of autoregressive functions. The macrocoefficients of the simplified specification are separated into three components, namely, a true value, an aggregation bias, and an implicit sampling error. Relative importance of these components is studied by using empirical data on a sample of 40 Canadian firms. The notion of aggregation gain of Grunfeld and Griliches is also examined.
Statistical theory of the analysis of experimental designs
Authors: Ogawa J. 
Published: June 2006

Abstract(Abstracts of papers presented at the Annual Meeting of the Statistical Science Association of Canada; May 29, 30, 31, 1975. / R$eAsum$eAs des articles pr$eAsent$eAs au congres annuel; 29, 30, 31 mai 1975; University of Alberta, Edmonton, Alberta.)
Statistical problems in multi‐variate (etiological) surveys
Authors: Sterling Theodor D. 
Published: June 2006

AbstractA number of multi‐variate etiological surveys are analyzed for recurrent sources of bias in balanced and purposive sampling designs. Three nonsampling components emerge that may dominate the total error of a sample survey estimate. Outstanding among these appear to be administrative consideration of cost and convenience which may actually determine a sampling procedure, especially by reliance on voluntary participation, proxy responses and case‐finding methods that restrict the sample. Next is lack of comparability of population series that differ on some initial state (as to smoking). Finally, errors are caused by strong beliefs in what results should be. Extensive experience has now shown that it may not be possible to conduct a satisfactory etiological inquiry by use of surveys using nonrandom population samples.
On the validation of fiducial techniques
Authors: Plante André 
Published: June 2006

AbstractA structured model is essentially a family of random vectorsXθdefined on a probability space with values in a sample space. If, for a given sample valuexand for each ω in the probability space, there is at most one parameter value θ for whichXθ(ω) is equal tox, then the model is called additive atx.When a certain conditional distribution exists, a frequency interpretation specific to additive structured models holds, and is summarized in a unique structured distribution for the parameter. Many of the techniques used by Fisher in deriving and handling his fiducial probability distribution are shown to be valid when dealing with a structured distribution.
A percentile regression model with an application to error frequency in group conversation tests
Authors: Liski Erkki P. , Puntanen Simo 
Published: June 2006

AbstractIn many applications, estimates of percentile curves interest investigators more than the mean regression line. We consider the situation where the dependent variable is a compound Poisson variable. Explicit parametric assumptions are made using a linear model for scale and heterogeneous variance. The model is applied to the data on group conversation tests in spoken English.
Pooling operators with the marginalization property
Authors: Genest Christian 
Published: June 2006

AbstractIn this paper, we consider the problem of combining a number of opinions which have been expressed as probability measures P1, …, Pn, over some space. It is shown that a pooling formula which has the marginalization property of McConway (1981) must be of the form T = Σni=1Wi Pi + (1 ‐ Σni =1Wi)Q, where Q is an arbitrary measure and W1, …, Wn ϵ [—1,1] are weights such that| ΣJ Σ j wj | ≤ 1 for every subset J of {1, …, n}. If, in addition, T is required to preserve the independence of arbitrary events A and B whenever these events are independent under each Pi, then either T = Pi for some 1 ≤ i ≤ n or T = Q, in which case Q takes values in {0, l}.
A comparison of two exact confidence regions for partially nonlinear regression models
Authors: Hamilton David 
Published: June 2006

AbstractExact confidence regions for all the parameters in nonlinear regression models can be obtained by comparing the lengths of projections of the error vector into orthogonal subspaces of the sample space. In certain partially nonlinear models an alternative exact region is obtained by replacing the linear parameters by their conditional estimates in the projection matrices. An ellipsoidal approximation to the alternative region is obtained in terms of the tangent‐plane coordinates, similar to one previously obtained for the more usual region. This ellipsoid can be converted to an approximate region for the original parameters and can be used to compare the two types of exact confidence regions.
Distribution of multivariate quadratic forms under certain covariance structures
Authors: Pavur Robert J. 
Published: June 2006

AbstractNecessary and sufficient conditions are given for the covariance structure of all the observations in a multivariate factorial experiment under which certain multivariate quadratic forms are independent and distributed as a constant times a Wishart. It is also shown that exact multivariate test statistics can be formed for certain covariance structures of the observations when the assumption of equal covariance matrices for each normal population is relaxed. A characterization is given for the dependency structure between random vectors in which the sample mean and sample covariance matrix have certain properties.
An example of ridge regression difficulties
Authors: Smith Gary 
Published: June 2006

AbstractA simple consumption function is used to illustrate two fundamental difficulties with ridge regression and similarly motivated procedures. The first is the ambiguity of multicollinearity measures for judging the data's “ill‐conditioning”. The second is the sensitivity of the estimates to the arbitrary normalization of the model. Neither of these poses a problem for least squares or Bayesian estimates. The logical restructuring of ridge procedures to avoid these difficulties leads to a more explicitly Bayesian approach.
Bounds for moments through a general orthogonal expansion in a pre‐hilbert space‐II
Authors: Mathai A. M. 
Published: June 2006

AbstractIn an earlier article [Canad. J. Statist., Vol, 3, No. 1, 1975, 13–34] bounds are obtained for the product moments of an arbitrary finite number of ordered random variables. These bounds are obtained with the help of a representation of an arbitrary function in terms of a complete orthonormal system in a pre‐Hilbert space of square integrable functions defined in a k‐dimensional unit cube. These results are extended to symmetric cases in this article.
On testing correlation between growth curves
Authors: Tan W. Y. 
Published: June 2006

AbstractFitting the growth curves by polynomials, this paper is intended to test whether or not there is any correlation between two characters. The likelihood ratio statistic is derived and is shown to be distributed under the null hypothesis as the product of three independent U statistics as defined in Anderson (1958). Box's procedure is then applied to approximate the critical region. A numerical example is given to illustrate the procedure.
A lower bound for the transition density function of a stochastic differential equation
Authors: Boyarsky A. 
Published: June 2006

AbstractLet Wt be a one‐dimensional Brownian motion on the probability space (Ω,F,P), and let dxt = a(xt)dt + b(xt)dwt, b2(x) > 0, be a one‐dimensional Ito stochastic differential equation. For a(x) = a0 + a1x + … + anxn on a bounded interval we obtain a lower bound for p(t,x,y), the transition density function of the homogeneous Markov process xt, depending directly on the coefficients a0,a1, …, an, and b(x).
An alternative proof of order statistics moment problem
Authors: Ali M. M. 
Published: June 2006

AbstractA necessary and sufficient condition that two distributions having finite means are identical is that for any fixed integer r > 0, the expected values of their rth (n ≫ r) order statistics are equal [or the expected values of their (n‐r)th (n > r ≫ 0) order statistics are equal] for all n where n is the sample size.
Clustering of correlation matrices: A step‐wise procedure
Authors: Wadycki Walter J. 
Published: June 2006

AbstractAn alternative to the criteria proposed by King [9] for clustering correlation matrices is explored. Several simple examples examined by total enumeration employing our criterion indicate that the criterion yields clusters with high within group correlation. A step‐wise routine is suggested for problems of realistic size and is applied to the examples presented in King's article. Additional evidence in the form of several Monte Carlo experiments indicates that the routine performs satisfactorily in determining the optimal separation of variables into two groups.
Measurements of separation among probability densities or random variables
Authors: Glick Ned 
Published: June 2006

AbstractDistance between two probability densities or two random variables is a well established concept in statistics. The present paper considers generalizations of distances to separation measurements for three or more elements in a function space. Geometric intuition and examples from hypothesis testing suggest lower and upper bounds for such measurements in terms of pairwise distances; but also in Lp spaces some useful non‐pairwise separation measurements always lie within these bounds. Examples of such separation measurements are the Bayes probability of correct classification among several arbitrary distributions, and the expected range among several random variables.
Structural probability bounds for the strong Pareto law
Authors: Dyer Danny 
Published: June 2006

AbstractBy using the structural density function (Fraser 1979, Ch. 7) of the parameters of a Pareto distribution, the structural distribution function of the strong Pareto law is derived. Its fractiles have been evaluated numerically for special cases, and the results are displayed through graphs from which structural one‐sided probability bounds may be found. It is shown that these graphs may also be used to find structural tolerance bounds for the Pareto distribution.
Testing the equality of the variances of two linear models
Authors: Chaubey Y. P. 
Published: June 2006

AbstractTesting the equality of variances of two linear models with common β‐parameter is considered. A test based on least squares residuals (ASR test) is proposed, and it is shown that this test is invariant under the group of scale and translation changes. For some special cases, it is also proved that this test has a monotone power function. Finding the exact critical values of this test is not easy; an approximation is given to facilitate the computation of these. The powers of the BLUS test, the F‐test and the new test are computed for various alternatives and compared in a particular case. The proposed test seems to be locally more powerful than the alternative tests.
Fourier smoother and additive models
Authors: Bilodeau Martin 
Published: June 2006

AbstractSuppose the observations (ti,yi), i = 1,… n, follow the modelwhere gj are unknown functions. The estimation of the additive components can be done by approximating gj, with a function made up of the sum of a linear fit and a truncated Fourier series of cosines and minimizing a penalized least‐squares loss function over the coefficients. This finite‐dimensional basis approximation, when fitting an additive model with r predictors, has the advantage of reducing the computations drastically, since it does not require the use of the backfitting algorithm. The cross‐validation (CV) [or generalized cross‐validation (GCV)] for the additive fit is calculated in a further 0(n) operations. A search path in the r‐dimensional space of degrees of freedom is proposed along which the CV (GCV) continuously decreases. The path ends when an increase in the degrees of freedom of any of the predictors yields an increase in CV (GCV). This procedure is illustrated on a meteorological data set.
Small‐sample intervals for regression
Authors: Tingley M. A. 
Published: June 2006

AbstractFor the general linear regression model Y = Xη + e, we construct small‐sample exponentially tilted empirical confidence intervals for a linear parameter 6 = aTη and for nonlinear functions of η. The coverage error for the intervals is Op(1/n), as shown in Tingley and Field (1990). The technique, though sample‐based, does not require bootstrap resampling. The first step is calculation of an estimate for η. We have used a Mallows estimate. The algorithm applies whenever η is estimated as the solution of a system of equations having expected value 0.We include calculations of the relative efficiency of the estimator (compared with the classical least‐squares estimate). The intervals are compared with asymptotic intervals as found, for example, in Hampel et at. (1986). We demonstrate that the procedure gives sensible intervals for small samples.
On the curtailment of sampling
Authors: Geisser Seymour 
Published: June 2006

AbstractWe address the problem of the curtailment or continuation of an experiment or trial at some interim point where say N observations are in hand and at least S > N observations had originally been scheduled for a decision. A Bayesian predictive approach is used to determine the probability that if one continued the trial with a further sample of size M where N +M ≥S, one would come to a particular decision regarding a parameter or a future observable. This point of view can also be applied to significance tests if one is willing to admit the calculation as a subjective assessment.
Linear statistics in change‐point estimation and their asymptotic behaviour
Authors: Rukhin A. L. 
Published: June 2006

AbstractABSTRACT The limiting behaviour of Bayes procedures in the asymptotic setting of the change‐point estimation problem is studied. It is shown that the distribution of the difference between the Bayes estimator and the parameter converges to the distribution of a fairly complicated random variable. A class of linear statistics is introduced, and the form of the Bayes estimator within this class is deduced. The asymptotic properties of this linear estimator are investigated in two different settings for the prior distribution.
Statistics on statistics: Measuring research productivity by journal publications between 1985 and 1995
Authors: Genest Christian 
Published: June 2006

AbstractSixteen international journals publishing statistical theory were surveyed over the 11‐year period beginning in 1985. Paper, author and adjusted page counts yield cursory measures of productivity for institutions and countries that contributed to fundamental statistical research during that period. These data clearly identify Canada as one of the main contributors to the development of the discipline in the past decade. They also provide valuable information on the evolution of publication habits, in terms of the volume of research, the length of papers, coauthorship practices, etc.
Time‐discrete beta‐process model for interval‐censored survival data
Authors: Sinha Debajyoti 
Published: June 2006

AbstractGrouped survival data with possible interval censoring arise in a variety of settings. This paper presents nonparametric Bayes methods for the analysis of such data. The random cumulative hazard, common to every subject, is assumed to be a realization of a Lévy process. A time‐discrete beta process, introduced by Hjort, is considered for modeling the prior process. A sampling‐based Monte Carlo algorithm is used to find posterior estimates of several quantities of interest. The methodology presented here is used to check further modeling assumptions. Also, the methodology developed in this paper is illustrated with data for the times to cosmetic deterioration of breast‐cancer patients. An extension of the methodology is presented to deal with two interval‐censored times in tandem data (as with some AIDS incubation data).