bluuebunny/crossref_metadata_2025 huggingface dataset:
A winning strategy for lotto games?
Authors: Joe Harry
Published: June 2006

In lotto games, the distribution of k‐tuples chosen by participants is not uniform, but the chance of any k‐tuple being the winner is the same. The winning categories consist of matching exactly k — i numbers from the winning k‐tuple for i = 0, 1, …, m for some m. The total prize pool for a category is divided equally among all the winning tickets in the category. Therefore the strategy of buying a ticket with a k‐tuple consisting of unpopular numbers will increase the expected amount of the prize if this k‐tuple is a winner in some category, because the prize pool is shared among fewer tickets. By modelling the distribution of 6‐tuples chosen by participants of Lotto 6/49 in Canada, the expected return and standard deviation of return can be computed. It is shown that the expected return can be more than the amount spent when the carryover is large, but the large standard deviation means that it would take tens of thousands of years to millions of years for the strategy to have a high probability of yielding a profit.
Asymptotic calculations of functions of expected values and covariances of order statistics
Authors: Stephens M. A.
Published: June 2006

Suppose m and V are respectively the vector of expected values and the covariance matrix of the order statistics of a sample of size n from a continuous distribution F. A method is presented to calculate asymptotic values of functions of m and V–1, for distributions F which are sufficiently regular. Values are given for the normal, logistic, and extreme‐value distributions; also, for completeness, for the uniform and exponential distributions, although for these other methods must be used.
Confidence intervals for parameters lying in a random polygon
Authors: Kolassa John E.
Published: June 2006

This paper presents algorithms for computing confidence intervals and regions for elements of a parameter vector when the signs of linear combinations of unknown parameters are observed, but the coefficients contain experimental error. These methods were proposed in the geochemical literature by Kolassa (1992) as a method specific to petrology. Experimental data are used to give linear constraints, involving quantities measured with error, on unknown free energies and entropies of a chemical reaction. Confidence intervals are given for these parameters, and these are compared with more naïve approaches.
Efficient bias‐Robust M‐Estimators of location in Kolmogorov normal neighbourhoods
Authors: Wu Eden K.H. , Zhou Julie
Published: June 2006

We consider minimax‐bias M‐estimation of a location parameter in a Kolmogorov neighbourhood K() of a normal distribution. The maximum asymptotic bias of M‐estimators for the Kolmogorov normal neighbourhood is derived, and its relation with the gross‐error sensitivity of the estimator at the nominal model (the Gaussian case) is found. In addition, efficient bias‐robust M‐estimators Ti are constructed. Numerical results are also obtained to show the percentage of increase in maximum asymptotic bias and the efficiency we can achieve for some well‐known ‐functions.
Approximating distributions of random functionals of Ferguson‐Dirichlet priors
Authors: Muliere Pietro , Tardella Luca
Published: June 2006

We explore the possibility of approximating the Ferguson‐Dirichlet prior and the distributions of its random functionals through the simulation of random probability measures. The proposed procedure is based on the constructive definition illustrated in Sethuraman (1994) in conjunction with the use of a random stopping rule. This allows us to set in advance the closeness to the distributions of interest. The distribution of the stopping rule is derived, and the practicability of the simulating procedure is discussed. Sufficient conditions for convergence of random functionals are provided. The numerical applications provided just sketch the idea of the variety of nonparametric procedures that can be easily and safely implemented in a Bayesian setting.
A technique for comparing graphical methods
Authors: Manchester Lise
Published: June 2006

The problem of assessing the merit of a technique for displaying data is addressed. The divergence, a measure of the difference between two graphs, is introduced and its properties discussed. The performance of the divergence is investigated by Monte Carlo methods. Two applications of the divergence are presented.
Combined Bayesian estimates for the equicorrelation coefficient
Authors: Viana Marios A.G.
Published: June 2006

Combined Bayesian estimates for equicorrelation covariance matrices are considered. The case of a common equicorrelation p and possibly different standard deviations σl,σk among k experimental groups is examined first, and the Bayesian estimation of (σ, σ1,σk) is discussed. Secondly, under the assumption of a common standard deviation and possibly different equicorrelations, the Bayesian estimation of (ρ1,ρk,σ) is considered.
Equivariant estimators of the covariance matrix
Authors: Perron François
Published: June 2006

Given a Wishart matrix S [S ∽ Wp(n, Σ)] and an independent multinomial vector X [X ∽ Np (μ, Σ)], equivariant estimators of Σ are proposed. These estimators dominate the best multiple of S and the Stein‐type truncated estimators.
Gaussian deconvolution via differentiation
Authors: Masry Elias , Rice John A.
Published: June 2006

Suppose that ϕ is a Gaussian density and that g = f * ϕ, where * denotes convolution. From observations with density g, one wishes to estimate f. We analyze an estimate which is a linear combination of estimates of derivatives of g and show that this estimate converges in an L2 norm at a rate which is compatible with the pointwise optimal rate established by Fan (1991).
One‐step M‐estimators in the linear model, with dependent errors
Authors: Field Christopher A. , Wiens Douglas P.
Published: June 2006

We consider the problem of robust M‐estimation of a vector of regression parameters, when the errors are dependent. We assume a weakly stationary, but otherwise quite general dependence structure. Our model allows for the representation of the correlations of any time series of finite length.

We first construct initial estimates of the regression, scale, and autocorrelation parameters. The initial autocorrelation estimates are used to transform the model to one of approximate independence. In this transformed model, final one‐step M‐estimates are calculated.

Under appropriate assumptions, the regression estimates so obtained are asymptotically normal, with a variance‐covariance structure identical to that in the case in which the autocorrelations are known a priori. The results of a simulation study are given. Two versions of our estimator are compared with the L1 ‐estimator and several Huber‐type M‐estimators. In terms of bias and mean squared error, the estimators are generally very close. In terms of the coverage probabilities of confidence intervals, our estimators appear to be quite superior to both the L1‐estimator and the other estimators. The simulations also indicate that the approach to normality is quite fast.
The Lindsay transform of natural exponential families
Authors: Kokonendji C. C. , Seshadri V.
Published: June 2006

Let μ be an infinitely divisible positive measure on R. If the measure ρμ is such that x‐2[ρμ(dx)—ρμ({0})δ0(dx)] is the Lévy measure associated with μ and is infinitely divisible, we consider for all positive reals α and β the measure Tα,β(μ) which is the convolution of μ*α and ρμ*β. For example, if μ is the inverse Gaussian law, then ρμ is a gamma law with paramter 3/2. Then Tα,β(μ) is an extension of the Lindsay transform of the first order, restricted to the distributions which are infinitely divisible. The main aim of this paper is to point out that it is possible to apply this transformation to all natural exponential families (NEF) with strictly cubic variance functions P. We then obtain NEF with variance functions of the form □ΔP(□Δ), where A is an affine function of the mean of the NEF. Some of these latter types appear scattered in the literature.
Correction locale de l'estimateur à noyau de la densité d'une loi de probabilité
Authors: Abdous Belkacem , Berlin ÈT Alain
Published: June 2006

The standard Parzen‐Rosenblatt kernel density estimator is known to systematically deviate from the true value near critical points of the density curve. To overcome this difficulty, we extend the Rao‐Blackwell method by using locally sufficient statistics: we define a new estimator and study its asymptotic behaviour. The interest of the method is shown by means of simulations.
On the asymptotic normality of the L1‐ and L2‐errors in histogram density estimation
Authors: Beirlant Jan , Györfi László , Lugosi Gábor
Published: June 2006

The L1 and L2‐errors of the histogram estimate of a density f from a sample X1,X2,…,Xn using a cubic partition are shown to be asymptotically normal without any unnecessary conditions imposed on the density f. The asymptotic variances are shown to depend on f only through the corresponding norm of f. From this follows the asymptotic null distribution of a goodness‐of‐fit test based on the total variation distance, introduced by Györfi and van der Meulen (1991). This note uses the idea of partial inversion for obtaining characteristic functions of conditional distributions, which goes back at least to Bartlett (1938).
On the admissibility of the maximum‐likelihood estimator of the binomial variance
Authors: Brown Lawrence D. , Chow Mosuk , Fong Duncan K.H.
Published: June 2006

This paper addresses the admissibility of the maximum‐likelihood estimator (MLE) of the variance of a binomial distribution with parameters n and p under squared‐error loss. We show that the MLE is admissible for n ≤ 5 and inadmissible for n≥ 6.
Consistency of least‐squares estimator and its jackknife variance estimator in nonlinear models
Authors: Shao Jun
Published: June 2006

The purpose of this paper is twofold: (1) We establish the consistency of the least‐squares estimator in a nonlinear modelyi = f(xi,θ) +σiei where the range of the parameter θ is noncompact, the regression function is unbounded, and the σi,'s are not necessarily equal. This extends the results in Jennrich (1969) and Wu (1981). (2) Under the same model, the jackknife estimator of the asymptotic covariance matrix of the least‐squares estimator is shown to be consistent, which provides a theoretical justification of the empirical results in Duncan (1978) and the use of the jackknife method in large‐sample inferences.
M‐Estimates of regression when the scale is unknown and the error distribution is possibly asymmetric: A minimax result
Authors: Li Bing , Zamar Ruben H.
Published: June 2006

Huber (1964) found the minimax‐variance M‐estimate of location under the assumption that the scale parameter is known; Li and Zamar (1991) extended this result to the case when the scale is unknown. We consider the robust estimation of the regression coefficients (β1,…,βp) when the scale and the intercept parameters are unknown. The minimax‐variance estimates of (β1,…,βp) with respect to the trace of their asymptotic covariance matrix are derived. The maximum is taken over ϵ‐contamination neighbourhoods of a central regression model with Gaussian errors (asymmetric contamination is allowed), and the minimum is taken over a large class of generalized M‐estimates of regression of the Mallow type. The optimal choice of estimates for the nuisance parameters (scale and intercept) is also considered.
The asymptotic behaviour of a class of L.‐estimators under long‐range dependence
Authors: Mukherjee Kanchan
Published: June 2006

This paper obtains asymptotic representations of a class of L‐estimators in a linear regression model when the errors are a function of long‐range‐dependent Gaussian random variables. These representations are then used to address some of the efficiency robustness properties of L‐estimators compared to the least‐squares estimator. It is observed that under the Gaussian error distribution, each member of the class has the same asymptotic efficiency as that of the least‐squares estimator. The results are obtained as a consequence of the asymptotic uniform linearity of some weighted empirical processes based on long‐range‐dependent random variables.
Quasi‐universal bandwidth selection for kernel density estimators
Authors: Wegkamp Marten H.
Published: June 2006

Let f̂n, h denote the kernel density estimate based on a sample of size n drawn from an unknown density f. Using techniques from L2 projection density estimators, the author shows how to construct a data‐driven estimator f̂n, h which satisfies

This paper is inspired by work of Stone (1984), Devroye and Lugosi (1996) and Birge and Massart (1997).
Statistical consequences of an adaptive treatment allocation in a clinical trial
Authors: Farewell V.T. , Viveros R. , Sprott D.A.
Published: June 2006

An examination of the likelihood arising from data in experiments comparing two therapies for neonatal respiratory failure illustrates a difficulty in the use of a design with adaptive treatment allocation as opposed to the use of the classical fixed‐sample‐size design. The requirement of obtaining objective scientific evidence as to which treatment is better for use on future patients conflicts with attempts to give the treatment thought to be the best to the patient at hand.
A characterization of the efficiency of individualized logistic regressions
Authors: Bull Shelley B. , Donner Allan
Published: June 2006

The use of individualized regressions, which reduces the polychotomous logistic regression model to several dichotomous models, has been proposed as a solution to some practical difficulties for binary covariates (Begg and Gray 1984, Biometrika, 71, 11–18). Its disadvantages, however, include loss of efficiency and the complexity of making comparisons among regressions. Using expressions for the large‐sample distribution of the maximum‐likelihood estimates, the efficiency of the individualized procedure relative to the polychotomous procedure is evaluated for the case in which the covariates are assumed to follow a multivariate normal distribution. The relative efficiency when the logistic slope vectors from different regressions are collinear can be substantially lower compared to the efficiency with orthogonal slope vectors. Further evaluations for binary covariates using collinear and orthogonal slope parametrizations lead to a similar characterization.
A modified bootstrap estimator for the mean of an asymmetric distribution
Authors: Swanepoel J.W.H. , BEER C.F. De
Published: June 2006

A modified bootstrap estimator of the population mean is proposed which is a convex combination of the sample mean and sample median, where the weights are random quantities. The estimator is shown to be strongly consistent and asymptotically normally distributed. The small‐ and moderate‐sample‐size behavior of the estimator is investigated and compared with that of the sample mean by means of Monte Carlo studies. It is found that the newly proposed estimator has much smaller mean squared errors and also yields significantly shorter confidence intervals for the population mean.
Detecting the interaction of quantity and quality: A study of Fisher's analysis
Authors: Fick Gordon Hilton
Published: June 2006

In Sections 49 and 50 of the Design of Experiments, Fisher discusses an experiment designed to compare the effects of several types of manure on yield. Each type of manure is applied at three dosage levels: zero, single, and double doses. Fisher points out that the usual contrasts constructed for a factorial experiment are unsatisfactory in this setting. In particular, since the response curves necessarily meet at the zero dose, the usual notion of interaction as a lack of parallelism cannot apply. Fisher then gives an appropriate definition for interaction in this setting. This paper is concerned with a class of orthogonal polynomials that can be used as an aid in the detection of this modified definition of interaction.
Robust Bayesian analysis of the binomial empirical Bayes problem
Authors: Sivaganesan S. , Berger James
Published: June 2006

Empirical Bayes estimation is considered for an i.i.d. sequence of binomial parameters θi arising from an unknown prior distribution G(.). This problem typically arises in industrial sampling, where samples from lots are routinely used to estimate the lot fraction defective of each lot. Two related issues are explored. The first concerns the fact that only the first few moments of G are typically estimable from the data. This suggests consideration of the interval of estimates (e.g., posterior means) corresponding to the different possible G with the specified moments. Such intervals can be obtained by application of well‐known moment theory. The second development concerns the need to acknowledge the uncertainty in the estimation of the first few moments of G. Our proposal is to determine a credible set for the moments, and then find the range of estimates (e.g., posterior means) corresponding to the different possible G with moments in the credible set.
A note on the likelihood‐ratio statistic under model misspecification
Authors: Viraswami K. , Reid N.
Published: June 2006

Following Viraswami and Reid (1996), higher‐order results under model misspecification are obtained for the likelihood‐ratio statistic and the adjusted likelihood‐ratio statistic, for the case of a scalar parameter. An improved version of the adjusted likelihood‐ratio statistic is suggested.
Robust sequential designs for approximately linear models
Authors: Wiens Douglas P.
Published: June 2006

We consider the problem of the sequential choice of design points in an approximately linear model. It is assumed that the fitted linear model is only approximately correct, in that the true response function contains a nonrandom, unknown term orthogonal to the fitted response. We also assume that the parameters are estimated by M‐estimation. The goal is to choose the next design point in such a way as to minimize the resulting integrated squared bias of the estimated response, to order n‐1. Explicit applications to analysis of variance and regression are given. In a simulation study the sequential designs compare favourably with some fixed‐sample‐size designs which are optimal for the true response to which the sequential designs must adapt.
Paradoxes in nonparametric tests
Authors: Haunsperger Deanna B.
Published: June 2006

This paper is a continuation of one (1992) in which the author studied the paradoxes that can arise when a nonparametric statistical test is used to give an ordering of k samples and the subsets of those samples. This article characterizes the projection paradoxes that can occur when using contingency tables, complete block designs, and tests of dichotomous behaviour of several samples. This is done by examining the “dictionaries” of possible orderings of each of these procedures. Specifically, it is shown that contingency tables and complete block designs, like the Kruskal‐Wallis nonparametric test on k samples, minimize the number and kinds of projection paradoxes that can occur; however, using a test of dichotomous behaviour of several samples does not. An analysis is given of two procedures used to determine the ordering of a pair of samples from a set of k samples. It is shown that these two procedures may not have anything in common.
Edgeworth expansion and the bootstrap for stratified sampling without replacement from a finite population
Authors: Chen Jiahua , Sitter R. R.
Published: June 2006

An Edgeworth expansion for a linear combination of stratum means in stratified sampling without replacement from a finite population is derived. The expansion is applied to a bootstrap proposed for this context to show that the bootstrap captures the second‐order term of the expansion.
A note on bias reduction in variable‐kernel density estimates
Authors: Mckay Ian
Published: June 2006

It is well known that the inverse‐square‐root rule of Abramson (1982) for the bandwidth h of a variable‐kernel density estimator achieves a reduction in bias from the fixed‐bandwidth estimator, even when a nonnegative kernel is used. Without some form of “clipping” device similar to that of Abramson, the asymptotic bias can be much greater than O(h4) for target densities like the normal (Terrell and Scott 1992) or even compactly supported densities. However, Abramson used a nonsmooth clipping procedure intended for pointwise estimation. Instead, we propose a smoothly clipped estimator and establish a globally valid, uniformly convergent bias expansion for densities with uniformly continuous fourth derivatives. The main result extends Hall's (1990) formula (see also Terrell and Scott 1992) to several dimensions, and actually to a very general class of estimators. By allowing a clipping parameter to vary with the bandwidth, the usual O(h4) bias expression holds uniformly on any set where the target density is bounded away from zero.
Including structural measurement errors in the nonlinear regression analysis of clustered data
Authors: Zidek J. V. , Le N. D. , Wong H. , Burnett R. T.
Published: June 2006

This paper extends methods for nonlinear regression analysis that have developed for the analysis of clustered data. Its novelty lies in its dual incorporation of random cluster effects and structural error in the measurement of the explanatory variables. Moments up to second order are assumed to have been specified for the latter to enable a generalized estimating equations approach to be used for fitting and testing nonlinear models linking response to these explanatory variables and random effects. Taylor expansion methods are used, and a difficulty with earlier approaches overcome. Finally we describe an application of this methodology to indicate how it can be used. That application concerns the degree of association of hospital admissions for acute respiratory health problems and air pollution.
A method for constructing supersaturated designs and its Es2 optimality
Authors: Tang Boxin , Wu C. F. J.
Published: June 2006

A lower bound for the Es2 value of an arbitrary supersaturated design is derived. A general method for constructing supersaturated designs is proposed and shown to produce designs with n runs and m = k(n — 1) factors that achieve the lower bound for Es2 and are thus optimal with respect to the Es2 criterion. Within the class of designs given by the construction method, further discrimination can be made by minimizing the pairwise correlations and using the generalized D and A criteria proposed by Wu (1993). Efficient designs of 12, 16, 20 and 24 runs are constructed by following this approach.
Asymptotic properties of a generalized regression‐type predictor of a finite population variance in probability sampling
Authors: Shah D. N. , Patel P. A.
Published: June 2006

A system of predictors for estimating a finite population variance is defined and shown to be asymptotically design‐unbiased (ADU) and asymptotically design‐consistent (ADC) under probability sampling. An asymptotic mean squared error (MSE) of a generalized regression‐type predictor, generated from the system, is obtained. The suggested predictor attains the minimum expected variance of any design‐unbiased estimator when the superpopulation model is correct. The generalized regression‐type predictor and the predictor suggested by Mukhopadhyay (1990) are compared.
Un critère de choix de variables en analyse en composantes principales fondé sur des modèles graphiques gaussiens particuliers
Authors: Falguerolles Antoine De , Jmel SaïD
Published: June 2006

We focus on the problem of selection of a subset of the variables so as to preserve the multivariate data structure that a principal‐components analysis of the initial variables would reveal. We propose a new method based on some adapted Gaussian graphical models. This method is then compared with those developed by Bonifas et al. (1984) and Krzanowski (1987a, b). It appears that the criteria for all methods consider the same correlation submatrices and often lead to similar results. The proposed approach offers some guidance as to the number of variables to be selected. In particular, Akaike's information criterion is used.
Mutability of DNA base pairs: A statistical approach based on linear discrimination
Authors: Leong Phaik Mooi , Morgenthaler Stephan
Published: June 2006

A mutational spectrum measures the frequency of mutations at particular base pairs along a given DNA sequence under the influence of a particular treatment. Such spectra have been measured for several genes, in various organisms and under different treatments. This article presents a method of analysis based on the statistical discrimination between mutable base pairs and stable base pairs. The coefficients of the discriminant function characterize the mutational spectrum induced by a given treatment on a given sequence. These coefficients can be interpreted by the user without specialized statistical knowledge.
Statistical analysis of right‐censored failure‐time data with partially specified hazard rates
Authors: Qin Jing , Sun Jianguo
Published: June 2006

This paper discusses the analysis of right‐censored failure‐time data in which the failure rate may have different forms in different time intervals. Such data occur naturally, for example, in demography studies and leukemia research, and a number of methods for the analysis have been proposed in the literature. However, most methods are purely parametric or nonparametric. Matthews and Farewell (1982), for example, discussed this problem and proposed a method for testing a constant failure rate against a failure rate involving a change point. To estimate an absolute limit on the attainable human life span, Zelterman (1992) discussed a hazard function that has different parametric forms over different time intervals. We consider a different situation in which the hazard function may follow a parametric form before a change point and is completely unknown after the change point. To test the existence of the change point, a modified maximal‐censored‐likelihood‐ratio test is proposed and its asymptotic properties are studied. A bootstrap method is described for finding critical values of the proposed test. Simulation results indicate that the test performs well.
The likelihood ratio under noncontiguous alternatives
Authors: Gombay Edit
Published: June 2006

The asymptotic distribution of the likelihood ratio under noncontiguous alternatives is shown to be normal for the exponential family of distributions. The rate of convergence of the parameters to the hypothetical value is specified where the asymptotic noncentral chi‐square distribution no longer holds. It is only a little slower than \documentclass{article}\pagestyle{empty}\begin{document}$\O\left( {n^{ - \frac{1}{2}} } \right)$\end{document}. The result provides compact power approximation formulae and is shown to work reasonably well even for moderate sample sizes.
Bayes and admissibility properties of estimators in truncated parameter spaces
Authors: Charras Alec , Van Eeden Constance
Published: June 2006

For the problem of estimating a parameter θ when θ is known to lie in a closed, convex subset D of Rk, conditions are given under which estimators δ of θ cannot be Bayes estimators, as well as conditions under which δ is inadmissible. The estimators considered are so‐called “boundary estimators”. Maximum‐likelihood estimators in truncated parameter spaces are examples to which our results often apply. For the special case when k = 1 and D is compact, two classes of estimators dominating the inadmissible ones are constructed. Some examples are given.
A nonparametric test for Type I censored data: r samples
Authors: Atkinson G.F. , Mount K.
Published: June 2006

This paper is concerned with testing that r random samples are all from the same population when the data are left (or right) censored. A statistic is developed which has elements of a goodness‐of‐fit statistic and of a modified Kruskal‐Wallis statistic. The efficiency of this statistic relative to some other commonly used statistics is calculated. Some Monte Carlo comparisons are given.
The least concave majorant of the empirical distribution function
Authors: Carolan Christopher A.
Published: June 2006

The author compares two estimators of a continuous, concave distribution function having support on the positive half line. In terms of samples from uniform distributions, he gives stochastic bounds for the pointwise and sup‐norm differences between the least concave majorant of the empirical distribution function and the underlying distribution function. He also offers evidence demonstrating the almost paradoxical result that the empirical distribution function is not as good an estimator as its least concave majorant in terms of sup‐norm error but a better pointwise estimator of the true distribution function in terms of mean squared error.
Sur la convergence des tests de Schlee et de Yatchew
Authors: Diack Cheikh A. T.
Published: June 2006

L'auteur adapte l'approche de Schlee (1980) et de Yatchew (1992) à la construction de deux tests de convexité pour une fonction de régression dans un modèle non paramétrique. Il établit de nouveaux résultats de convergence pour ces tests et étudie leur comportement dans de petits échantillons à l'aide de simulations.
Directional mixture models and optimal estimation of the mixing density
Authors: Kim Peter , Koo Ja‐Yong
Published: June 2006

The authors develop consistent nonparametric estimation techniques for the directional mixing density. Classical spherical harmonics are used to adapt Euclidean techniques to this directional environment. Minimax rates of convergence are obtained for rotation ally invariant densities verifying various smoothness conditions. It is found that the differences in smoothness between the Laplace, the Gaussian and the von Mises‐Fisher distributions lead to contrasting inferential conclusions.
Empirical likelihood for linear regression models under imputation for missing responses
Authors: Wang Qihua , Rao J. N. K.
Published: June 2006

The authors study the empirical likelihood method for linear regression models. They show that when missing responses are imputed using least squares predictors, the empirical log‐likelihood ratio is asymptotically a weighted sum of chi‐square variables with unknown weights. They obtain an adjusted empirical log‐likelihood ratio which is asymptotically standard chi‐square and hence can be used to construct confidence regions. They also obtain a bootstrap empirical log‐likelihood ratio and use its distribution to approximate that of the empirical log‐likelihood ratio. A simulation study indicates that the proposed methods are comparable in terms of coverage probabilities and average lengths of confidence intervals, and perform better than a normal approximation based method.
Robustness properties of minimally‐supported Bayesian D‐optimal designs for heteroscedastic models
Authors: Dette Holger , Song Dale , Wong Weng Kee
Published: June 2006

Bayesian D‐optimal designs supported on a fixed number of points were found by Dette & Wong (1998) for estimating parameters in a polynomial model when the error variance depends exponentially on the explanatory variable. The present authors provide optimal designs under a broader class of error variance structures and investigate the robustness properties of these designs to model and prior distribution assumptions. A comparison of the performance of the optimal designs relative to the popular uniform designs is also given. The authors' results suggest that Bayesian D‐optimal designs suported on a fixed number of points are more likely to be globaly optimal among all designs if the prior distribution is symmetric and is concentrated around its mean.
Restricted minimax robust designs for misspecified regression models
Authors: Heo Giseon , Schmuland Byron , Wiens Douglas P.
Published: June 2006

The authors propose and explore new regression designs. Within a particular parametric class, these designs are minimax robust against bias caused by model misspecification while attaining reasonable levels of efficiency as well. The introduction of this restricted class of designs is motivated by a desire to avoid the mathematical and numerical intractability found in the unrestricted minimax theory. Robustness is provided against a family of model departures sufficiently broad that the minimax design measures are necessarily absolutely continuous. Examples of implementation involve approximate polynomial and second order multiple regression.
On the robustness of empirical likelihood ratio confidence intervals for location
Authors: Tsao Min , Zhou Julie
Published: June 2006

The authors examine the robustness of empirical likelihood ratio (ELR) confidence intervals for the mean and M‐estimate of location. They show that the ELR interval for the mean has an asymptotic breakdown point of zero. They also give a formula for computing the breakdown point of the ELR interval for M‐estimate. Through a numerical study, they further examine the relative advantages of the ELR interval to the commonly used confidence intervals based on the asymptotic distribution of the M‐estimate.
Asymptotic behaviour of M‐estimators in AR(p) models under nonstandard conditions
Authors: El Bantli Faouzi , Hallin Marc
Published: June 2006

The authors derive the limiting distribution of M‐estimators in AR(p) models under nonstandard conditions, allowing for discontinuities in score and density functions. Unlike usual regularity assumptions, these conditions are satisfied in the context of L1‐estimation and autoregression quantiles. The asymptotic distributions of the resulting estimators, however, are not generally Gaussian. Moreover, their bootstrap approximations are consistent along very specific sequences of bootstrap sample sizes only.
A wavelet method for unfolding sphere size distributions
Authors: Antoniadis Anestis , Fan Jianqing , Gijbels Irène
Published: June 2006

The authors consider the problem of estimating the density of the radii of spheres in a medium, based on their observed random cross‐sections. This problem is known as Wicksell's corpuscle problem. The authors first convert Wicksell's integral equation to a form suitable for the application of thresholding wavelet methods to solve ill‐posed integral equations, given noisy data. They then derive the asymptotic properties of their estimators and compare them with other methods available via a Monte Carlo simulation study. They also illustrate their approach with some real data.
Consistency of semiparametric maximum likelihood estimators for two‐phase sampling
Authors: Vaart Aad Van Der , Wellner Jon A.
Published: June 2006

Semiparametric maximum likelihood estimators have recently been proposed for a class of two‐phase, outcome‐dependent sampling models. All of them were “restricted” maximum likelihood estimators, in the sense that the maximization is carried out only over distributions concentrated on the observed values of the covariate vectors. In this paper, the authors give conditions for consistency of these restricted maximum likelihood estimators. They also consider the corresponding unrestricted maximization problems, in which the “absolute” maximum likelihood estimators may then have support on additional points in the covariate space. Their main consistency result also covers these unrestricted maximum likelihood estimators, when they exist for all sample sizes.
Generalized orthogonal contrast tests for homogeneity of ordered means
Authors: Mcdermott Michael P.
Published: June 2006

Several procedures have been proposed for testing equality of ordered means. The best‐known of these is the likelihood‐ratio test introduced by Bartholomew, which possesses generally superior power characteristics to those of its competitors. Difficulties in implementing this test have led to the development of alternative approaches, such as tests based on single and multiple contrasts. Some recent approaches have utilized approximations to the polyhedral cone defining the restricted parameter space, including those of Akkerboom (circular cone) and Mudholkar & McDermott (orthant). This article proposes a class of tests based on an improved orthant approximation to the polyhedral cone. These tests may be viewed as generalizations of the orthogonal contrast test proposed by Mukerjee, Robertson & Wright. Studies of the power functions of several competing tests indicate that the generalized orthogonal contrast tests are effective alternatives to the likelihood‐ratio test, especially when the latter is difficult to implement.
The effect of nonnormality on near‐replicate lack‐of‐fit tests
Authors: Bedrick Edward J. , Christensen Ronald
Published: June 2006

We examine the effect of nonnormality on the distributions of near‐replicate lack‐of‐fit F‐tests. We show that when the number of clusters is large, the distributions of the lack‐of‐fit tests depend on the kurtosis of the error distribution, and that heavy‐tailed error distributions can inflate significantly the sizes of the tests. This behaviour is also evident in small samples, where some lack‐of‐fit tests are clearly more affected than others by nonnormality. Two modifications of the F‐tests are suggested to eliminate the effect of the kurtosis on the limiting null distributions, and their behaviour is studied for small samples.
Estimation du comportement asymptotique des autocovariances et autocorrelations empiriques de processus multivariéeas
Authors: Et Alain Berlinet , Franco Christian
Published: June 2006

Statistics based on the sample autocovariances are widely used in time‐series analysis. Estimators of the asymptotic covariance between the sample autocovariances are commonly derived from the so‐called Bartlett's formula. However, this formula essentially holds for linear processes. This entails that for a wide range of nonlinear time series the above‐mentioned estimators are not suitable. In this paper the behaviour of an alternative estimator is studied within the framework of centered or uncentered multivariate strongly mixing processes. Applications to differential functions of sample autocovariances, such as the sample autocorrelations, are considered.
Error reduction in density estimation under shape restrictions
Authors: Rychlik Tomasz
Published: June 2006

For the problems of nonparametric estimation of nonincreasing and symmetric unimodal density functions with bounded supports we determine the projections of estimates onto the convex families of possible parent densities with respect to the weighted integrated squared error. We also describe the method of approximating the analogous projections onto the respective density classes satisfying some general moment conditions. The method of projections reduces the estimation errors for all possible values of observations of a given finite sample size in a uniformly optimal way and provides estimates sharing the properties of the parent densities.
Deux méthodes d'estimation pour les paramètres de processus moyenne mobile spatiaux
Authors: Adjengue Luc D. , Moore Marc
Published: June 2006

Nous considérons les processus de type moyenne mobile spatiaux, {Xs, s ∈ 𝕋}, oú 𝕋 est un treillis triangulaire du plan ℝ2. Afin d'estimer les paraètres de tels processus, Adjengue & Moore (1993) ont étudié la méthode de vraisemblance et celle de la pseudo‐vraisemblance gaussienne. Nous étudions ici deux autres mé'thodes. La première est basée sur l'estimation des corrélations et le lien entre celles‐ci et les paramètres du processus. La deuxième repose sur une approximation linéaire optimale du processus. Les propriétés asymptotiques des estimateurs proposés sont analysées et comparées. Des simulations permettent d'observer le comportement des estimateurs dans le cas d'échantillons de tailles fixes.
A cluster problem as defined by nearest neighbours
Authors: Enns E. G. , Ehlers P. F. , Misi T.
Published: June 2006

Randomly generated points in Rd are connected to their nearest neighbours (Euclidean distance). The resulting connected clusters of points are studied. This paper examines questions related to the collection of clusters formed and to the internal structure of a cluster. In particular, the one‐dimensional structure is examined in detail.
Lois limites pour les statistiques d'ordre dans le cas non identiquement distribue
Authors: Belbachir Mohammadine
Published: June 2006

Let X1, X2,… be a sequence of independent random variables with distribution functions F1, where 1 ≤ i ≤ n, and for each n ≥ 1 let X1,n ≤… ≤ Xn,n denote the order statistics of the first n random variables. Under suitable hypotheses about the F1, we characterize the limit distribution functions H(x) for which P(Xk,n ⩽ anx + bn) → H(x), where an > 0 and bn are real constants. We consider the cases where κ = κ(n) satisfies √n {κ(n)/n — λ} → 0 and √n {κ(n)/n — λ} → ∞ separately.
Minimax weights for generalised M‐estimation in biased regression models
Authors: Sinha Sanjoy , Wiens Douglas P.
Published: June 2006

The authors consider the construction of weights for Generalised M‐estimation. Such weights, when combined with appropriate score functions, afford protection from biases arising through incorrectly specified response functions, as well as from natural variation. The authors obtain minimax fixed weights of the Mallows type under the assumption that the density of the independent variables is correctly specified, and they obtain adaptive weights when this assumption is relaxed. A simulation study indicates that one can expect appreciable gains in precision when the latter weights are used and the various sources of model uncertainty are present.
On the simultaneous effects of model misspecification and errors in variables
Authors: Gustafson Paul
Published: June 2006

Model misspecification and noisy covariate measurements are two common sources of inference bias. There is considerable literature on the consequences of each problem in isolation. In this paper, however, the author investigates their combined effects. He shows that in the context of linear models, the large‐sample error in estimating the regression function may be partitioned in two terms quantifying the impact of these sources of bias. This decomposition reveals trade‐offs between the two biases in question in a number of scenarios. After presenting a finite‐sample version of the decomposition, the author studies the relative impacts of model misspecification, covariate imprecision, and sampling variability, with reference to the detectability of the model misspecification via diagnostic plots.
Goodness‐of‐fit tests for parametric models based on biased samples
Authors: Sun Yanqing , Cui Sufang , Tiwari Ram C.
Published: June 2006

The authors study the problem of checking the adequacy of a parametric model for a distribution using several possibly censored weight biased samples. They discuss identifiability problems related to the underlying distribution and the distributions of the biased samples. They propose a test statistic based on the supremum of the weighted aggregated martingale residual processes from a number of such samples. Both numerical and graphical procedures are discussed, which the authors apply to do model checking for oil exploration drilling data.
Erythematous Plaque Studded With Papules and Pustules in an Immunocompetent Patient
Authors: Shahin Ahmad B. , Idriss Munir H. , Lehman Julia S.
Published: March 2024

A male patient was evaluated by the dermatology inpatient consult service after a 5-week history of a skin lesion on the right anterior thigh with intermittent itching and mild tenderness to palpation. What is your diagnosis?
The selenoenzyme type I iodothyronine deiodinase: a new tumor suppressor in ovarian cancer
Authors: Alfandari Adi , Moskovich Dotan , Weisz Avivit , Katzav Aviva , Kidron Debora , Beiner Mario , Josephy Dana , Asali Aula , Hants Yael , Yagur Yael , Weitzner Omer , Ellis Martin , Itchaki Gilad , Ashur‐Fabian Osnat
Published: March 2024

The selenoenzyme type I iodothyronine deiodinase (DIO1) catalyzes removal of iodine atoms from thyroid hormones. Although DIO1 action is reported to be disturbed in several malignancies, no work has been conducted in high‐grade serous ovarian carcinoma (HGSOC), the most lethal gynecologic cancer. We studied DIO1 expression in HGSOC patients [The Cancer Genome Atlas (TCGA) data and tumor tissues], human cell lines (ES‐2 and Kuramochi), normal Chinese hamster ovarian cells (CHO‐K1), and normal human fallopian tube cells (FT282 and FT109). To study its functional role, DIO1 was overexpressed, inhibited [by propylthiouracil (PTU)], or knocked down (KD), and cell count, proliferation, apoptosis, cell viability, and proteomics analysis were performed. Lower DIO1 levels were observed in HGSOC compared to normal cells and tissues. TCGA analyses confirmed that low DIO1 mRNA expression correlated with worse survival and therapy resistance in patients. Silencing or inhibiting the enzyme led to enhanced ovarian cancer proliferation, while an opposite effect was shown following DIO1 ectopic expression. Proteomics analysis in DIO1‐KD cells revealed global changes in proteins that facilitate tumor metabolism and progression. In conclusion, DIO1 expression and ovarian cancer progression are inversely correlated, highlighting a tumor suppressive role for this enzyme and its potential use as a biomarker in this disease.
Dual role of GRHL3 in bladder carcinogenesis depending on histological subtypes
Authors: Lammert Franziska C. , Pannhausen Julia , Noetzel Erik , Friedland Florian , Wirtz Julia , Herfs Yannick , Leypold Sophie , Gan Lin , Weiskirchen Ralf , Schnitzler Tician , Knüchel Ruth , Maurer Jochen , Jonigk Danny D. , Rose Michael , Gaisa Nadine T.
Published: March 2024

The effect of grainyhead‐like transcription factor 3 (GRHL3) on cancer development depends on the cancer subtypes as shown in tumor entities such as colorectal or oral squamous cell carcinomas. Here, we analyzed the subtype‐specific role of GRHL3 in bladder carcinogenesis, comparing common urothelial carcinoma (UC) with squamous bladder cancer (sq‐BLCA). We examined GRHL3 mRNA and protein expression in cohorts of patient samples, its prognostic role and its functional impact on tumorigeneses in different molecular and histopathological subtypes of bladder cancer. We showed for GRHL3 a reverse expression in squamous and urothelial bladder cancer subtypes. Stably GRHL3‐overexpressing EJ28, J82, and SCaBER in vitro models revealed a tumor‐suppressive function in squamous and an oncogenic role in the urothelial cancer cells affecting cell and colony growth, and migratory and invasive capacities. Transcriptomic profiling demonstrated highly subtype‐specific GRHL3‐regulated expression networks coined by the enrichment of genes involved in integrin‐mediated pathways. In SCaBER, loss of ras homolog family member A (RHOA) GTPase activity was demonstrated to be associated with co‐regulation of eukaryotic translation initiation factor 4E family member 3 (EIF4E3), a potential tumor suppressor gene. Thus, our data provide for the first time a detailed insight into the role of the transcription factor GRHL3 in different histopathological subtypes of bladder cancer.