bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and analysis of computer experiments when the output is highly correlated over the input space
Authors: Lim Yong B. , Sacks Jerome , Studden W. J. , Welch William J. 
Published: June 2006

AbstractTo build a predictor, the output of a deterministic computer model or “code” is often treated as a realization of a stochastic process indexed by the code's input variables. The authors consider an asymptotic form of the Gaussian correlation function for the stochastic process where the correlation tends to unity. They show that the limiting best linear unbiased predictor involves Lagrange interpolating polynomials; linear model terms are implicitly included. The authors then develop optimal designs based on minimizing the limiting integrated mean squared error of prediction. They show through several examples that these designs lead to good prediction accuracy.
Bounds on the maximum number of clear two‐factor interactions for 2m‐p designs of resolution III and IV
Authors: Tang Boxin , Ma Fengshi , Ingram Debra , Wang Hong 
Published: June 2006

AbstractThe authors derive upper and lower bounds on the maximum number of clear two‐factor interactions in 2m−p fractional factorial designs of resolution III and IV. A two‐factor interaction is said to be clear if it is not aliased with any main effect or with any other two‐factor interaction. The lower bounds are obtained by exhibiting specific designs. By comparing the bounds with the values of the maximum number of clear two‐factor interactions in cases where it is known, one concludes that the construction methods perform quite well.
Median estimation through a regression transformation
Authors: Yang Zhenlin 
Published: June 2006

AbstractThe author considers the problem of constructing confidence intervals for the median of a future observation at certain values of exogenous variables, following a normalizing transformation. He shows that when this transformation is estimated, the usual interval obtained through an inverse transformation needs to be corrected, even when the sample size is large. He then gives a simple analytical solution to this problem and provides simulation results confirming the good small‐sample properties of the corrected interval. He also presents two concrete illustrations.
Minimax A‐ and D‐optimal integer‐valued wavelet designs for estimation
Authors: Oyet Alwell J. 
Published: June 2006

AbstractThe author discusses integer‐valued designs for wavelet estimation of nonparametric response curves in the possible presence of heteroscedastic noise using a modified wavelet version of the Gasser‐Müller kernel estimator or weighted least squares estimation. The designs are constructed using a minimax treatment and the simulated annealing algorithm. The author presents designs for three case studies in experiments for investigating Gompertz's theory on mortality rates, nitrite utilization in bush beans and the impact of crash helmets in motorcycle accidents.
Stabilizing bootstrap‐tconfidence intervals for small samples
Authors: Polansky Alan M. 
Published: June 2006

AbstractA major use of the bootstrap methodology is in the construction of nonparametric confidence intervals. Although no consensus has yet been reached on the best way to proceed, theoretical and empirical evidence indicate that bootstra.‐tintervals provide a reasonable solution to this problem. However, when applied to small data sets, these intervals can be unusually wide and unstable. The author presents techniques for stabilizing bootstra.‐tintervals for small samples. His methods are motivated theoretically and investigated though simulations.
Score tests for zero inflation in generalized linear models
Authors: Deng Dianliang , Paul Sudhir R. 
Published: June 2006

AbstractThe authors develop score tests of goodness of fit for discrete generalized linear models against zero inflation. The binomial and Poisson models are treated as examples, and in the latter case the proposed test reduces to that of Broek (1995). Some simulation results and an illustrative example are presented.
Inférence par les martingales pour des processus ponctuels à compensateur discontinu
Authors: Dauxois Jean‐Yves 
Published: June 2006

AbstractL'auteur introduit un modèle d'intensité mutltiplicative généralisé pour les processus ponctuels à compensateur discontinu. Il en étudie l'inférence par les martingales et obtient un résultat de normalité asymptotique. Il applique ces résultats à l'estimateur de Nelson‐Aalen du taux de panne cumulé dans le cas de données de durée de vie censuées et de loi non nécessairement continues.
Monte Carlo Kalman filter and smoothing for multivariate discrete state space models
Authors: Song Peter Xue‐Kun 
Published: June 2006

AbstractThe author studies state space models for multivariate binomial time series, focussing on the development of the Kalman filter and smoothing for state variables. He proposes a Monte Carlo approach employing the latent variable representation which transplants the classical Kalman filter and smoothing developed for Gaussian state space models to discrete models and leads to a conceptually simple and computationally convenient approach. The method is illustrated through simulations and concrete examples.
Nonparametric two‐step regression estimation when regressors and error are dependent
Authors: Pinkse Jons 
Published: June 2006

AbstractThis paper considers estimation of the functiongin the modelYt=g(Xt) + ϵtwhen E(ϵt|Xt) ≠ 0 with nonzero probability. We assume the existence of aninstrumental variable Ztthat is independent of ϵt, and of aninnovationηt=Xt—E(Xt|Zt). We use a nonparametric regression ofXtonZtto obtain residuals ηt, which in turn are used to obtain a consistent estimator ofg. The estimator was first analyzed by Newey, Powell & Vella (1999) under the assumption that the observations are independent and identically distributed. Here we derive a sample mean‐squared‐error convergence result for independent identically distributed observations as well as a uniform‐convergence result under time‐series dependence.
Nonparametric regression for threshold data
Authors: Müller Ursula U. 
Published: June 2006

AbstractConsider a detector which records the times at which the endogenous variable of a nonparametric regression model exceeds a certain threshold. If the error distribution is known, the regression function can still be identified from these threshold data. The author constructs estimators for the regression function that are transformations of kernel estimators. She determines the bandwidth that minimizes the asymptotic mean average squared error. Her investigation was motivated by recent work on stochastic resonance in neuroscience and signal detection theory, where it was observed that detection of a subthreshold signal is enhanced by the addition of noise. The author compares her model with several others that have been proposed in the recent past.
Bayesian assessment of goodness of fit against nonparametric alternatives
Authors: Conigliani Caterina , Castro J. Iván , O'HAGAN Anthony 
Published: June 2006

AbstractThe classical chi‐square test of goodness of fit compares the hypothesis that data arise from some parametric family of distributions, against the nonparametric alternative that they arise from some other distribution. However, the chi‐square test requires continuous data to be grouped into arbitrary categories. Furthermore, as the test is based upon an approximation, it can only be used if there are sufficient data. In practice, these requirements are often wasteful of information and overly restrictive. The authors explore the use of the fractional Bayes factor to obtain a Bayesian alternative to the chi‐square test when no specific prior information is available. They consider the extent to which their methodology can handle small data sets and continuous data without arbitrary grouping.
Estimating the number of clusters
Authors: Cuevas Antonio , Febrero Manuel , Fraiman Ricardo 
Published: June 2006

AbstractHartigan (1975) defines the numberqof clusters in ad‐variate statistical population as the number of connected components of the set {f> c}, wherefdenotes the underlying density function on Rdandcis a given constant. Some usual cluster algorithms treatqas an input which must be given in advance. The authors propose a method for estimating this parameter which is based on the computation of the number of connected components of an estimate of {f> c}. This set estimator is constructed as a union of balls with centres at an appropriate subsample which is selected via a nonparametric density estimator off. The asymptotic behaviour of the proposed method is analyzed. A simulation study and an example with real data are also included.
On the distribution of linear combinations of the components of a Dirichlet random vector
Authors: Provost Serge B. , Cheong Young‐Ho 
Published: June 2006

AbstractOn making use of a result of Imhof, an integral representation of the distribution function of linear combinations of the components of a Dirichlet random vector is obtained. In fact, the distributions of several statistics such as Moran and Geary's indices, the Cliff‐Ord statistic for spatial correlation, the sample coefficient of determination, F‐ratios and the sample autocorrelation coefficient can be similarly determined. Linear combinations of the components of Dirichlet random vectors also turn out to be a key component in a decomposition of quadratic forms in spherically symmetric random vectors. An application involving the sample spectrum associated with series generated by ARMA processes is discussed.
Implications of random cut‐points theory for the Mann‐Whitney and binomial tests
Authors: Edwardes Michael D.Deb. 
Published: June 2006

AbstractThrough random cut‐points theory, the author extends inference for ordered categorical data to the unspecified continuum underlying the ordered categories. He shows that a random cut‐point Mann‐Whitney test yields slightly smaller p‐values than the conventional test for most data. However, when at least P% of the data lie in one of the k categories (with P = 80 for k = 2, P = 67 for k = 3,…, P = 18 for k = 30), he also shows that the conventional test can yield much smaller p‐values, and hence misleadingly liberal inference for the underlying continuum. The author derives formulas for exact tests; for k = 2, the Mann‐Whitney test is but a binomial test.
A nonparametric test of conditional autoregressive heteroscedasticity for threshold autoregressive models
Authors: Chen Min , Chen Gemai 
Published: June 2006

AbstractThreshold autoregressive models are widely used in time‐series applications. When building or using such a model, it is important to know whether conditional heteroscedasticity exists. The authors propose a nonparametric test of this hypothesis. They develop the large‐sample theory of a test of nonlinear conditional heteroscedasticity adapted to nonlinear autoregressive models and study its finite‐sample properties through simulations. They also provide percentage points for carrying out this test, which is found to have very good power overall.
Extrapolation of subsampling distribution estimators: The i.i.d. and strong mixing cases
Authors: Bertail Patrice , Politis Dimitris N. 
Published: June 2006

AbstractPolitis & Romano (1994) proposed a general subsampling methodology for the construction of large‐sample confidence regions for an arbitrary parameter under minimal conditions. Nevertheless, the subsampling distribution estimators may sometimes be inefficient (in the case of the sample mean of i.i.d. data, for instance) as compared to alternative estimators such as the bootstrap and/or the asymptotic normal distribution (with estimated variance). The authors investigate here the extent to which the performance of subsampling distribution estimators can be improved by interpolation and extrapolation techniques, while at the same time retaining the robustness property of consistent distribution estimation even in nonregular cases; both i.i.d. and weakly dependent (mixing) observations are considered.
Flexible regression modeling
Authors: Hooper Peter M. 
Published: June 2006

AbstractThe author proposes a new method for flexible regression modeling of multi‐dimensional data, where the regression function is approximated by a linear combination of logistic basis functions. The method is adaptive, selecting simple or more complex models as appropriate. The number, location, and (to some extent) shape of the basis functions are automatically determined from the data. The method is also affine invariant, so accuracy of the fit is not affected by rotation or scaling of the covariates. Squared error and absolute error criteria are both available for estimation. The latter provides a robust estimator of the conditional median function. Computation is relatively fast, particularly for large data sets, so the method is well suited for data mining applications.
Estimation of relaxation time distributions in magnetic resonance imaging
Authors: Susko Edward , Bronskill Michael J. , Graham Simon J. , Tibshirani Robert J. 
Published: June 2006

AbstractMagnetic resonance imaging techniques can be used to measure some biophysical properties of tissue. In this context, the T2 relaxation time is an important parameter for soft‐tissue contrast. The authors develop a new technique to estimate the integral of the distribution of T2 relaxation time without imposing any constraint other than the monotonicity of the underlying cumulative relaxation time distribution. They explore the properties of the estimation and its applications for the analysis of breast tissue data. As they show, an extension of linear discriminant analysis is found to distinguish well between two classes of breast tissue.
On the polynomial structural relationship
Authors: Huang Y. H. Steve , Huwang Longcheen 
Published: June 2006

AbstractIn estimating a linear measurement error model, extra information is generally needed to identify the model. Here the authors show that the polynomial structural model with errors in the endogenous and exogenous variables can be identified without any extra information if the degree is greater than one. They also show that a weighted least squares approach for the estimation of the parameters in the model leads to the same estimates as the solutions of a system of estimating equations.
An adaptive randomized design with application to estimation
Authors: Melfi Vincent F. , Page Connie , Geraldes Margarida 
Published: June 2006

AbstractWhen allocating observations to two populations for estimation or testing, the optimal proportion of the data that should be allocated to the first population, if it exists, often depends on unknown parameters. Adaptive designs have thus been proposed, in which allocation of the next observation is based on an estimate of the optimal proportion computed from the data already gathered. The authors introduce a simple randomized adaptive design and give some of its properties. Applications are given to estimating the difference of two success probabilities, and the difference of two normal means.
Variance estimation for the finite population distribution function with complete auxiliary information
Authors: Wu Changbao , Sitter Randy R. 
Published: June 2006

AbstractThe authors develop jackknife and analytical variance estimators for the estimator of Chambers & Dunstan (1986) and Rao, Kovar & Mantel (1990) of the finite population distribution function, using complete auxiliary information. They also describe the associated model and show the design consistency of the variance estimators, whose small‐sample performance is examined through a limited simulation study. They highlight the operational advantages of the jackknife in the model‐based setting of Chambers & Dunstan (1986) and its better conditional performance in the design‐based setting of Rao, Kovar & Mantel (1990).
Statistical analyses for round robin interaction data
Authors: Gill Paramjit S. , Swartz Tim B. 
Published: June 2006

AbstractThis paper considers the analysis of round robin interaction data whereby individuals from a group of subjects interact with one another, producing a pair of outcomes, one for each individual. The authors provide an overview of the various analyses applied to these types of data and extend the work in several directions. In particular, they provide a fully Bayesian analysis for such data and use a real data example for illustration purposes.
A glimpse of the impact of pál erd#ous on probability and statistics
Authors: CsõRGõ MiklÓS 
Published: June 2006

AbstractThe title of this article notwithstanding, it is the author's aspiration here to provide a bit more than merely a glimpse of some of Erdõs's contributions per se to probability‐statistics. He hopes to have succeeded in providing a guided tour of, and whenever it has appeared feasible, an introduction to, a few selected areas that have been strongly influenced by the work of Erdõs. The author also hopes to have succeeded in facilitating a glimpse of the impact of these contributions by presenting them in their historical context.
Quotient spaces and statistical models
Authors: Mccullagh Peter 
Published: June 2006

AbstractThe purpose of this paper is to draw attention to the widespread occurrence of quotient spaces in statistical work. Quotient spaces are intrinsic to probability distributions, residuals and interaction in linear models, covariance functions and variograms of stochastic processes, etc. The theme is that explicit recognition of the quotient space can offer surprising conceptual simplification. The advantages of working directly with the quotient space are hard to describe in general. As the examples demonstrate, the answer lies partly in directness of approach.
Asymptotic behavior of the grenander estimator at density flat regions
Authors: Carolan Chris , Dykstra Richard 
Published: June 2006

AbstractOver forty years ago, Grenander derived the MLE of a monotone decreasing density f with known mode. Prakasa Rao obtained the asymptotic distribution of this estimator at a fixed point x where f' (x) < 0. Here, we obtain the asymptotic distribution of this estimator at a fixed point x when f is constant and nonzero in some open neighborhood of x. This limiting distribution is expressible as the convolution of a closed‐form density and a rescaled standard normal density. Groeneboom (1983) derived the aforementioned closed‐form density and we provide an alternative, more direct derivation.
Semiparametric Bayesian inference for regression models
Authors: Seifu Yodit , Severini Thomas A. , Tanner Martin A. 
Published: June 2006

AbstractThis paper presents a method for Bayesian inference for the regression parameters in a linear model with independent and identically distributed errors that does not require the specification of a parametric family of densities for the error distribution. This method first selects a nonparametric kernel density estimate of the error distribution which is unimodal and based on the least‐squares residuals. Once the error distribution is selected, the Metropolis algorithm is used to obtain the marginal posterior distribution of the regression parameters. The methodology is illustrated with data sets, and its performance relative to standard Bayesian techniques is evaluated using simulation results.
Conditional logspline density estimation
Authors: Mâacsse Benoît R. , Truong Young K. 
Published: June 2006

AbstractInconditional logspline modelling, the logarithm of the conditional density function, logf(y|x), is modelled by using polynomial splines and their tensor products. The parameters of the model (coefficients of the spline functions) are estimated by maximizing the conditional log‐likelihood function. The resulting estimate is a density function (positive and integrating to one) and is twice continuously differentiable. The estimate is used further to obtain estimates of regression and quantile functions in a natural way. An automatic procedure for selecting the number of knots and knot locations based on minimizing a variant of the AIC is developed. An example with real data is given. Finally, extensions and further applications of conditional logspline models are discussed.
Fisher information in weighted distributions
Authors: Iyengar Satish , Kvam Paul , Singh Harshinder 
Published: June 2006

AbstractSuppose that a density fθ (x) belongs to an exponential family, but that inference about θ must be based on data that are obtained from a density that is proportional to W(x)fθ(x). The authors study the Fisher information about θ in observations obtained from such weighted distributions and give conditions under which this information is greater than under the original density. These conditions involve the hazard‐ and reversed‐hazard‐rate functions.
A similarity analysis of curves
Authors: Maldonado Yolanda MuÑoz , Staniswalis Joan G. , Irwin Louis N. , Byers Donna 
Published: June 2006

AbstractThe authors propose a method for comparing two samples of curves. The notion of similarity between two curves is the basis of three statistics they suggest for testing the null hypothesis of no difference between the two groups. They exploit standard tools from functional data analysis to preprocess the observed curves and use the permutation distribution under the null hypothesis to obtain p‐values for their tests. They explore the operating characteristics of these tests through simulations and as an application, compare the ganglioside distribution in brain tissue between old and young rats.
Estimation of a residual distribution with small numbers of repeated measurements
Authors: Susko Edward , Nadon Robert 
Published: June 2006

AbstractThe authors consider the estimation of a residual distribution for different measurement problems with a common measurement error process. The problem is motivated by issues arising in the analysis of gene expression data but should have application in other similar settings. It is implicitly assumed throughout that there are large numbers of measurements but small numbers of repeated measurements. As a consequence, the distribution of the estimated residuals is a biased estimate of the residual distribution. The authors present two methods for the estimation of the residual distribution with some restriction on the form of the distribution. They give an upper bound for the rate of convergence for an estimator based on the characteristic function and compare its performance with that of another estimator with simulations.
A pseudo‐empirical best linear unbiased prediction approach to small area estimation using survey weights
Authors: You Yong , Rao J. N. K. 
Published: June 2006

AbstractThe authors develop a small area estimation method using a nested error linear regression model and survey weights. In particular, they propose a pseudo‐empirical best linear unbiased prediction (pseudo‐EBLUP) estimator to estimate small area means. This estimator borrows strength across areas through the model and makes use of the survey weights to preserve the design consistency as the area sample size increases. The proposed estimator also has a nice self‐benchmarking property. The authors also obtain an approximation to the model mean squared error (MSE) of the proposed estimator and a nearly unbiased estimator of MSE. Finally, they compare the proposed estimator with the EBLUP estimator and the pseudo‐EBLUP estimator proposed by Prasad & Rao (1999), using data analyzed earlier by Battese, Harter & Fuller (1988).