bluuebunny/crossref_metadata_2025_split huggingface dataset:
The asymptotic distribution of the likelihood ratio when the model is incorrect
Authors: Foutz Robert V. , Srivastava R.C. 
Published: June 2006

AbstractClassical results on the asymptotic distribution of the likelihood ratio statistic rely on the assumption that the model chosen to construct the test statistic be correct. The model is said to be correct if it contains the true distribution of the observations. In this paper the asymptotic distribution of the likelihood ratio statistic is derived without the condition that the model need be correct.
An analysis of hourly deposition data
Authors: Switzer Paul 
Published: June 2006

AbstractA scavenging‐type model is propsed for the analysis of hourly data of nitrate or other pollutants in precipitation. The model would, in principle, allow for the separate estimation of the cloud‐water pollutant contribution and the air‐column contribution. In turn, these may be interpreted as regional and local contributions. As an illustration, the model was fitted to data from four precipitation events. Estimation of the model parameters was done on an event‐specific basis except for pooling of the scavenging rate parameter.
Estimation of regression equation with cauchy disturbances
Authors: Kadiyala K.R. , Murthy K.S.R. 
Published: June 2006

AbstractIn this paper we present two methods of estimating a linear regression equation with Cauchy disturbances. The first method uses the maximum likelihood principle and therefore the estimators obtained are consistent. The asymptotic covariance is derived which provides with the necessary statistics for the purpose of making inference in large samples. The second method is the method of least lines which minimizes the sum of absolute errors (MSAE) from the fitted regression. Then these two methods are compared through a Monte Carlo study. The maximum likelihood method emerges superior over the MSAE method. However, the MSAE procedure which does not depend on the distribution of the error term appears to be a close competitor to the maximum likelihood estimator.
Generalized manova double linear hypothesis with double linear restrictions
Authors: Kabe D. G. 
Published: June 2006

AbstractFor the generalized MANOVA model of Potthoff and Roy [7], Gleser and Olkin [3] give a likelihood ratio test criterion for testing double linear parametric functions of the regression parameters. Their theory is extended in this paper to the testing of double linear parametric functions with double linear restrictions on the parameters. The theory is presented in terms of the original variates unlike Gleser and Olkin [3] who resort to canonical transformations of the original variates.
An easy method of constructing latin square designs balanced for the immediate residual and other order effects
Authors: Sharma V. K. 
Published: June 2006

AbstractBradley (1958) proposed a very simple procedure for constructing latin square designs to counterbalance the immediate sequential effect for an even number of treatments. When the number of treatments is odd, balance in a single latin square is not possible. In the present note we have developed an analogous method for the construction of such designs which may be used for an even or odd number of treatments. A proof has also been offered to assure the general validity of the procedure.
The design of a clinical trial with several patient categories
Authors: Donner Allan 
Published: June 2006

AbstractVarious strategies are investigated for selecting one of two medical treatments when patients may be divided into k ≥ 2 categories on the basis of their expected differences in response to the two treatments. The strategies compared are (1) k independent decisions, (2) a single overall decision made on the basis of simple pooling, and (3) the Bayes strategy. The optimal clinical trial size is supplied for each strategy, and conditions delineated under which each of the strategies is to be preferred.
Convergence of compensators of simple point processes
Authors: Schiopu‐Kratina Ioana 
Published: June 2006

AbstractProcesses are viewed as random variables with values in the space of càdlàg functions endowed with the J1 topology. For a sequence of point processes with their minimal filtration, the convergence of their compensators is studied. It is shown that convergence in probability of the processes and of the corresponding conditional distributions of their jump times implies, with some additional hypothesis, the convergence in probability of the compensators. The result is then applied to convergence to a quasi‐left‐continuous point process with independent increments.
A model for generalized edit and imputation of survey data
Authors: Giles Philip 
Published: June 2006

AbstractThe concept of generalized systems for survey processing has gained importance in recent years because of resource constraints. Statistics Canada has adopted a policy to develop generalized systems to the extent that is feasible and cost‐efficient. A generalized edit and imputation (E&I) system is of prime importance. An E&I system is proposed that describes the overall edit and imputation function as comprising five component functions: edit specification, edit analysis, edit application, error localization, and imputation. Implementation of the E&I model, especially for numerical data, is discussed. The focus is on methodology rather than systems.
Monotonicity of certain multivariate normal probabilities
Authors: Eaton Morris L. , Perlman Michael D. 
Published: June 2006

AbstractLet (X, Y) have a (p+q)‐dimensional normal distribution and let C, K be convex symmetric sets of dimensions p, q respectively. Under certain restrictions on the mean vector it is shown that P(X ε C, Y ε K) is a monotonically increasing function of the first canonical correlation coefficient between X and Y, provided the remaining coefficients are zero.
On a model for reciprocity failure in photographic materials
Authors: Anderson William J. 
Published: June 2006

AbstractThe photographic emulsion is not a pure energy detector. For either very high or very low intensities of the incident light, the efficiency of an emulsion in terms of resulting photographic density is not as great as intermediate levels of intensity. Such behaviour is called reciprocity law failure.In this article a simple probabilistic model for reciprocity failure is presented which incorporates both high and low intensity failure. The flexibility of the model inherent in its simplicity allows more sophisticated models to be constructed.
Simultaneous estimation of the hardy‐weinberg proportions
Authors: Chow Mosuk , Fong Duncan K.H. 
Published: June 2006

AbstractWe consider the problem of simultaneously estimating k + 1 related proportions, with a special emphasis on the estimation of Hardy‐Weinberg (HW) proportions. We prove that the uniformly minimum‐variance unbiased estimator (UMVUE) of two proportions which are individually admissible under squared‐error loss are inadmissible in estimating the proportions jointly. Furthermore, rules that dominate the UMVUE are given. A Bayesian analysis is then presented to provide insight into this inadmissibility issue: The UMVUE is undesirable because the two estimators are Bayes rules corresponding to different priors. It is also shown that there does not exist a prior which yields the maximum‐likelihood estimators simultaneously. When the risks of several estimators for the HW proportions are compared, it is seen that some Bayesian estimates yield significantly smaller risks over a large portion of the parameter space for small samples. However, the differences in risks become less significant as the sample size gets larger.
A general lower bound of minimax risk for absolute‐error loss
Authors: Chen Jeesen 
Published: June 2006

AbstractA general lower bound of minimax risk for absolute‐error loss is given in terms of the Hellinger modulus of the estimation problem. The main results are applicable to various parametric, semi‐parametric and nonparametric problems. Two examples of parametric estimation problems and two examples of density estimation problems are given. In all of these examples, the general lower bound achieves the convergence rates of minimax risk.
Confidence regions for the common mean vector of several multivariate normal populations
Authors: Jordan Scott M. , Krishnamoorthy K. 
Published: June 2006

AbstractSuppose that there are independent samples available from several multivariate normal populations with the same mean vector m̈ but possibly different covariance matrices. The problem of developing a confidence region for the common mean vector based on all the samples is considered. An exact confidence region centered at a generalized version of the well‐known Graybill‐Deal estimator of m̈ is developed, and a multiple comparison procedure based on this confidence region is outlined. Necessary percentile points for constructing the confidence region are given for the two‐sample case. For more than two samples, a convenient method of approximating the percentile points is suggested. Also, a numerical example is presented to illustrate the methods. Further, for the bivariate case, the proposed confidence region and the ones based on individual samples are compared numerically with respect to their expected areas. The numerical results indicate that the new confidence region is preferable to the single‐sample versions for practical use.
Robust scale estimation in the error‐components model using the empirical characteristic function
Authors: Markatou Marianthi , Horowitz Joel L. 
Published: June 2006

AbstractRobust estimators of the scale parameters in the error‐components model are described. The new estimators are based on the empirical characteristic functions of appropriate sets of residuals and are affine equivariant, consistent and asymptotically normal. The robustness of the new estimators is investigated via influence‐function calculations. The results of Monte Carlo experiments and an example based on real data illustrate the usefulness of the estimators.
Air pollution effects on hospital admission rates: A random effects modeling approach
Authors: Burnett Richard , Krewski Daniel 
Published: June 2006

AbstractStatistical methods are proposed to analyze parallel time series of hospital‐based health data and measurements of ambient air pollution. Specifically, associations between the number of daily health events (hospital admissions or emergency‐room visits for respiratory illnesses) and daily levels of ambient air pollutants in the vicinity of several hospitals are examined. A relative‐risk regression model is proposed in which the regression parameters are assumed to vary at random among hospitals. Adjustment for seasonal trends in admissions are also considered. Simple computational methods based on generalized estimating equations are explored as the basis for statistical inference. The proposed methods are illustrated on data obtained from 164 acute‐care hospitals in Ontario over the May‐to‐August period for 1983 to 1988. These admission rates are related to ozone levels obtained from 22 monitoring stations maintained by the Ontario Ministry of the Environment.
Multivariate distributions with generalized inverse gaussian marginals, and associated poisson mixtures
Authors: Barndorff‐Nielsen O. E. , BlæSILD P. , Seshadri V. 
Published: June 2006

AbstractSeveral types of multivariate extensions of the inverse Gaussian (IG) distribution and the reciprocal inverse Gaussian (RIG) distribution are proposed. Some of these types are obtained as random‐additive‐effect models by means of well‐known convolution properties of the IG and RIG distributions, and they have one‐dimensional IG or RIG marginals. They are used to define a flexible class of multivariate Poisson mixtures.
Deconvolution with supersmooth distributions
Authors: Fan Jianqing 
Published: June 2006

AbstractNonparametric deconvolution problems require one to recover an unknown density when the data are contaminated with errors. Optimal global rates of convergence are found under the weighted Lp‐loss (1 ≤ p ≤ ∞). It appears that the optimal rates of convergence are extremely low for supersmooth error distributions. To resolve this difficulty, we examine how high the noise level can be for deconvolution to be feasible, and for the deconvolution estimate to be as good as the ordinary density estimate. It is shown that if the noise level is not too high, nonparametric Gaussian deconvolution can still be practical. Several simulation studies are also presented.
Resolving the conflict over fisher's exact test
Authors: Routledge R. D. 
Published: June 2006

AbstractFisher's exact test for two‐by‐two contingency tables has repeatedly been criticized as being too conservative. These criticisms arise most frequently in the context of a planned experiment for which the numbers of successes in each of two experimental groups are assumed to be binomially distributed. It is argued here that the binomial model is often unrealistic, and that the departures from the binomial assumptions reduce the conservatism in Fisher's exact test. Further discussion supports a recent claim of Barnard (1989) that the residual conservatism is attributable, not to any additional information used by the competing method, but to the discrete nature of the test, and can be drastically reduced through the use of Lancaster's mid‐p‐value. The binomial model is not recommended in that it depends on extra, questionable assumptions.
Distribution asymptotique des autocorrélations ďun processus saisonnier non stationnaire
Authors: Latour Alain , Roy Roch 
Published: June 2006

AbstractWe examine the behaviour of the sample autocorrelations of a seasonal time series for which the first difference of order s (s ≥ 1) is stationary. The asymptotic distribution of the autocorrelations r'(k) based on uncentered data and of the autocorrelations r(k) based on centered data are derived. In each case, the asymptotic distribution is characterized as a function of the lag k and the parameters of the process. A simulation study was conducted in order to investigate the rate of convergence of the finite sample distributions of r(k) and r'(k) to their asymptotic counterparts and to evaluate the effect of centering or not centering the data on the distribution of autocorrelations.
Regression for overdetermined systems: A fisheries example
Authors: Manchester L. , Field C. A. , Mcdougall A. 
Published: June 2006

AbstractIn trying to establish the relationship between a yearly fisheries recruitment series and meteorological or oceanographic variables such as air pressure or sea surface temperature, we are often faced with the situation where the number of regressors exceeds the number of observations. In this paper we use the techniques of penalized least squares and principal‐components regression to determine whether air pressure over the North Atlantic can be used to predict two North Atlantic cod recruitment series. The results suggest that penalized least squares can be very effective in these situations.
Distribution‐free subset selection for incompletely ranked data
Authors: Pan Guohua , Taam Winson 
Published: June 2006

AbstractIn market research and some other areas, it is common that a sample of n judges (consumers, evaluators, etc.) are asked to independently rank a series of k objects or candidates. It is usually difficult to obtain the judges' full cooperation to completely rank all k objects. A practical way to overcome this difficulty is to give each judge the freedom to choose the number of top candidates he is willing to rank. A frequently encountered question in this type of survey is how to select the best object or candidate from the incompletely ranked data. This paper proposes a subset selection procedure which constructs a random subset of all the k objects involved in the survey such that the best object is included in the subset with a prespecified confidence. It is shown that the proposed subset selection procedure is distribution‐free over a very broad class of underlying distributions. An example from a market research study is used to illustrate the proposed procedure.
Robust M‐estimators of scale: Minimax bias versus maximal variance
Authors: Collins John R. 
Published: June 2006

AbstractIn the location‐scale estimation problem, we study robustness properties of M‐estimators of the scale parameter under unknown ϵ‐contamination of a fixed symmetric unimodal error distribution F0. Within a general class of M‐estimators, the estimator with minimax asymptotic bias is shown to lie within the subclass of α‐interquantile ranges of the empirical distribution symmetrized about the sample median. Our main result is that as ϵ → 0, the limiting minimax asymptotic bias estimator is sometimes (e.g., when Fo is Cauchy), but not always, the median absolute deviation about the median. It is also shown that contamination in the neighbourhood of a discontinuity of the influence function of a minimax bias estimator can sometimes inflate the asymptotic variance beyond that achieved by placing all the ϵ‐contamination at infinity. This effect is quantified by a new notion of asymptotic efficiency that takes into account the effect of infinitesimal contamination of the parametric model for the error distribution.
Second‐order properties of intraclass correlation estimators for a symmetric normal distribution
Authors: Pal Nabendu , Lim Wooi K. 
Published: June 2006

AbstractConsider the problem of estimating the intraclass correlation coefficient of a symmetric normal distribution under the squared error loss function. The general admissibility of the standard estimators of the intraclass correlation coefficient is hard to check due to their complicated sampling distributions. We follow the asymptotic decision‐theoretic approach of Ghosh and Sinha (1981) and prove that the three standard intraclass correlation estimators (the maximum‐likelihood estimator, the method‐of‐moments estimator and the first‐order unbiased estimator) are second‐order admissible for all p ≥ 2, p being the dimension of the distribution.
Extended voting measures
Authors: Heard Andrew , Swartz Tim 
Published: June 2006

AbstractThis paper extends a number of voting measures used to quantify voting power. The extension is based on the recognition that individuals sometimes vote in coalitions. This observation gives rise to a statistical model which considers past voting patterns of subsets of eligible voters. The model is then used to obtain estimates of the probabilities of all voting combinations from which empirical measures are calculated. The calculation of the estimated probabilities may involve high‐dimensional integrations. An example is given based on past decisions arising from the Supreme Court of Canada.
Le problème d'Anscombe pour les lois binomiales négatives généralisées
Authors: Kokonendji Calestin C. 
Published: June 2006

AbstractConsider a finite sample from a generalized negative‐binomial distribution where both (canonical and index) parameters are unknown. This note proves that both the maximum‐likelihood estimate and the moment estimate of the index parameter exist if and only if the sample variance is greater than the sample mean. This extends a result for the negative‐binomial distribution that had been conjectured by Anscombe (1950) and later shown by Levin and Reeds (1977).
A class of shrinkage estimators in linear regression
Authors: Blaker Helge 
Published: June 2006

AbstractWe consider the problem of using shrinkage estimators that shrink towards subspaces in linear regression, in particular subspaces spanned by principal components. This is especially important when multicollinearity is present and the number of predictors is not small compared to the sample size. New theoretical results about Stein estimation are used to get estimators with lower theoretical risk than standard Stein estimators used by Oman (1991). Application of the techniques to real data is largely successful.
Simultaneous robust estimation of location and scale parameters: A minimum‐distance approach
Authors: Öztürk Ömer , Hettmansperger Thomas P. 
Published: June 2006

AbstractSimultaneous robust estimates of location and scale parameters are derived from minimizing a minimum‐distance criterion function. The criterion function measures the squared distance between the pth power (p > 0) of the empirical distribution function and the pth power of the imperfectly determined model distribution function over the real line. We show that the estimator is uniquely defined, is asymptotically bivariate normal and for p > 0.3 has positive breakdown. If the scale parameter is known, when p = 0.9 the asymptotic variance (1.0436) of the location estimator for the normal model is smaller than the asymptotic variance of the Hodges‐Lehmann (HL)estimator (1.0472). Efficiencies with respect to HL and maximum‐likelihood estimators (MLE) are 1.0034 and 0.9582, respectively. Similarly, if the location parameter is known, when p = 0.97 the asymptotic variance (0.6158) of the scale estimator is minimum. The efficiency with respect to the MLE is 0.8119. We show that the estimator can tolerate more corrupted observations at oo than at – for p < 1, and vice versa for p > 1.
Laplace approximations to posterior moments and marginal distributions on circles, spheres, and cylinders
Authors: Bagchi Parthasarathy , Kadane Joseph B. 
Published: June 2006

AbstractWe extend recent work on Laplace approximations (Tierney and Kadane 1986; Tierney, Kass, and Kadane 1989) from parameter spaces that are subspaces of Rk to those that are on circles, spheres, and cylinders. While such distributions can be mapped onto the real line (for example, a distribution on the circle can be thought of as a function of an angle θ, 0 ⩽ 0 ⩾ 2π), that the end points coincide is not a feature of the real line, and requires special treatment. Laplace approximations on the real line make essential use of the normal integral in both the numerator and the denominator. Here that role is played by the von Mises integral on the circle, by the Bingham integrals on the spheres and hyperspheres, and by the normal‐von Mises and normal‐Bingham integrals on the cylinders and hypercylinders, respectively. We begin with a brief introduction to Laplace approximations and to previous Bayesian work on circles, spheres, and cylinders. We then develop the theory for parameter spaces that are hypercylinders, since all other shapes considered here are special cases. We compute some examples, which show reasonable accuracy even for small samples.
Estimation of scale parameters in mixture distributions
Authors: Dey Dipak K. 
Published: June 2006

AbstractSimultaneous estimation of scale parameters is considered in mixture distributions under squared‐error loss. A general class of estimators is obtained which dominates the componentwise best multiple estimators and the moment estimators. As special cases, improved estimators are obtained for the multivariate t‐distribution and the p‐variate Lomax distribution.