bluuebunny/crossref_metadata_2025_split huggingface dataset:
Best linear corrector of classification estimates of proportions of objects in several unknown classes
Authors: Fortier Jean‐J. 
Published: June 2006

AbstractIt has been recognized that counting the objects allocated by a rule of classification to several unknown classes often does not provide good estimates of the true class proportions of the objects to be classified. We propose a linear transformation of these classification estimates, which minimizes the mean squared error of the transformed estimates over all possible sets of true proportions. This so‐called best‐linear‐corrector (BLC) transformation is a function of the confusion (classification‐error) matrix and of the first and second moments of the prior distribution of the vector of proportions. When the number of objects to be classified increases, the BLC tends to the inverse of the confusion matrix. The estimates that are obtained directly by this inverse‐confusion corrector (ICC) are also the maximum‐likelihood unbiased estimates of the probabilities that the objects originate from one or the other class, had the objects been preselected with those probabilities. But for estimating the actual proportions, the ICC estimates behave less well than the raw classification estimates for some collections. In that situation, the BLC is substantially superior to the ICC even for some large collections of objects and is always substantially superior to the raw estimates. The statistical model is applied concretely to the measure of forest covers in remote sensing.
Asymptotic properties of a conditional maximum‐likelihood estimator
Authors: Ferguson H. 
Published: June 2006

AbstractInference for a scalar interest parameter in the presence of nuisance parameters is considered in terms of the conditional maximum‐likelihood estimator developed by Cox and Reid (1987). Parameter orthogonality is assumed throughout. The estimator is analyzed by means of stochastic asymptotic expansions in three cases: a scalar nuisance parameter, m nuisance parameters from m independent samples, and a vector nuisance parameter. In each case, the expansion for the conditional maximum‐likelihood estimator is compared with that for the usual maximum‐likelihood estimator. The means and variances are also compared. In each of the cases, the bias of the conditional maximum‐likelihood estimator is unaffected by the nuisance parameter to first order. This is not so for the maximum‐likelihood estimator. The assumption of parameter orthogonality is crucial in attaining this result. Regardless of parametrization, the difference in the two estimators is first‐order and is deterministic to this order.
Maximum‐likelihood estimation for the removal method
Authors: Bedrick Edward J. 
Published: June 2006

AbstractWe prove that the profile log‐likelihood function for the removal method of estimating population size is unimodal. The result is obtained by a variation‐diminishing property of the Laplace transform. An implication of this result is that the likelihood‐ratio confidence region for the population size is always an interval. Necessary and sufficient conditions for the existence of a finite maximum‐likelihood estimator are presented. We also present evidence that the likelihood‐ratio confidence interval for the population size has acceptable small‐sample coverage properties.
On the relative accuracy of certain bootstrap procedures
Authors: Feuerverger Andrey , Robinson John , Wong Augustine 
Published: June 2006

AbstractWe show the second‐order relative accuracy, on bounded sets, of the Studentized bootstrap, exponentially tilted bootstrap and nonparametric likelihood tilted bootstrap, for means and smooth functions of means. We also consider the relative errors for larger deviations. Our method exploits certain connections between Edgeworth and saddlepoint approximations to simplify the computations.
Bayesian interim analysis of lifetime data
Authors: Papandonatos George D. , Geisser Seymour 
Published: June 2006

AbstractIn hypothesis testing involving censored lifetime data that are independently distributed according to an accelerated‐failure‐time model, it is often of interest to predict whether continuation of the experiment will significantly alter the inferences drawn at an interim point. Approaching the problem from a Bayesian viewpoint, we suggest a possible solution based on Laplace approximations to the posterior distribution of the parameters of interest and on Markov‐chain Monte Carlo. We apply our results to Weibull data from a carcinogenesis experiment on mice.
Robust F‐tests for linear models
Authors: Taplin Ross H. 
Published: June 2006

AbstractThe author presents a robust F‐test for comparing nested linear models. It is suggested that the approach will be attractive to practitioners because it is based on the familiar F‐statistic and corresponds to the common practice of reporting F‐statistics after removing obvious outliers. It is calibrated in terms of a real parameter that can be directly interpreted as the willingness of the data analyst to remove observations, and the sensitivity of the F‐statistic to this parameter is easily examined. The procedure is evaluated with a simulation study where a scale mixture distribution is used to generate outliers. The procedure is also applied to some data where the occurrence of an outlier is confounded with the significance of a regression term. This provides a comparison of two competing models for the data: one removing an outlier and the other including an additional regression term instead.
The digital rainbow: Some history and applications of numerical spectrum analysis
Authors: BRILLINGER David R. 
Published: June 2006

AbstractStatistical concepts and techniques are basic to scientific investigation. One concept that enjoys both a theoretical and a physical existence is the spectrum. A spectrum may be described as a display of the intensity or variability of a phenomenon versus period or frequency. Spectra are particularly useful in the study of systems subject to resonance, but have many other uses. This paper begins with some of the historical development of the field, describing a sequence of contributions by Michelson, Schuster, Einstein, Fisher, Bartlett, Tukey, and Whittle. The paper next presents collaborative applications to the study of the free oscillations of the earth, to the dispersion of seismic surface waves and to nuclear‐magnetic‐resonance spectroscopy. Finally, there is mention of open problems and opinions on future directions.
Higher‐order approximations for pitman estimators and for optimal compromise estimators
Authors: Ventura Laura 
Published: June 2006

AbstractLaplace approximations for the Pitman estimators of location or scale parameters, including terms O(n−1), are obtained. The resulting expressions involve the maximum‐likelihood estimate and the derivatives of the log‐likelihood function up to order 3. The results can be used to refine the approximations for the optimal compromise estimators for location parameters considered by Easton (1991). Some applications and Monte Carlo simulations are discussed.
On the estimation of the proportion of positives in a sequence of screening experiments
Authors: Bickis Mikelis , Bleuer Susana , Krewski Daniel 
Published: June 2006

AbstractA metaanalytic estimator of the proportion of positives in a sequence of screening experiments is proposed. The distribution‐free estimator is based on the empirical distribution of P‐values from individual experiments, which is uniform under the global null hypotheses of no positives in the sequence of experiments performed. Under certain regularity conditions, the proportion of positives corresponds to the derivative of this distribution under the alternative hypothesis of the existence of some positives. The statistical properties of the estimator are established, including its bias, variance, and rate of convergence to normality. Optimal estimators with minimum mean squared error are also developed under specific alternative hypotheses. The application of the proposed methods is illustrated using data from a sequence of screening experiments with chemicals to determine their carcinogenic potential.
Empirical‐distribution‐function goodness‐of‐fit tests for discrete models
Authors: Henze Norbert 
Published: June 2006

AbstractWe present a simple framework for studying empirical‐distribution‐function goodness‐of‐fit tests for discrete models. A key tool is a weak‐convergence result for an estimated discrete empirical process, regarded as a random element in some suitable sequence space. Special emphasis is given to the problem of testing for a Poisson model and for the geometric distribution. Simulations show that parametric bootstrap versions of the tests maintain a nominal level of significance very closely even for small samples where reliance upon asymptotic critical values is doubtful.
Estimation of the number of studies with positive trends when studies with negative trends are present
Authors: Crump Kenny S. , Krewski Daniel 
Published: June 2006

AbstractA two‐point estimator is proposed for the proportion of studies with positive trends among a collection of studies, some of which may demonstrate negative trends. The proposed estimator is the y‐intercept of the secant line joining the points (a, F̌(a)) and (b, F̌(b)), where F̌(p) is the empirical distribution function of p‐values from one‐tailed tests for positive trend derived from the individual studies. Although this estimator is negatively biased for any choice of the points 0 ≤ a < b ≤ 1, the bias is less than that of the previously proposed one‐point estimator defined by setting b = 1. The bias of the two‐point estimator is smallest when a and b approach the inflection point of the true distribution function, E [F̌(p)]. The utility of the two‐point estimator is demonstrated by using it to estimate the number of male‐mouse liver carcinogens among carcinogenicity studies conducted by the National Toxicology Program.
Statistical assessment of multivalued screening tests
Authors: Viana Marios A. G. , Rogatko André , Rebbeck Timothy R. 
Published: June 2006

AbstractWhen the outcome of a screening test is expressed by the probabilities of k possible outcomes among individuals with a certain physiologic condition and by the corresponding probabilities among individuals without the condition, the screening usefulness of the test depends on the relative likelihood that its result may properly alter the management of a given patient. New statistical methods are introduced to apply the screening usefulness concept to the assessment of combined or multivalued tests. The method is applied to assess the usefulness of genotypes at cytochrome P450 IAI and glutathione‐S‐transferase‐μ as biomarkers of susceptibility to developing lung cancer. The argument and methods developed should be widely applicable to the statistical assessment of screening tests for a wide range of physiologic conditions.
Integrated marked Poisson processes with application to image correlation spectroscopy
Authors: Benn A.G. , Kulperger R.J. 
Published: June 2006

AbstractImage correlation spectroscopy (ICS) is an experimental technique used in the study of cell tissue, specifically to obtain information about protein aggregates. This paper discusses a filtered Poisson process model of ICS, and uses this to obtain the sampling distribution of an estimator of interest, using spectral methods. Integrals of third‐ and fourth‐order spectra are involved, and methods proposed in the literature are studied numerically. They are found to be infeasible methods, requiring months of computing time. An alternative method is proposed and is illustrated on some actual ICS experiments. A novel feature in this study is the use of a SIMD‐class parallel computer, a MasPar MP‐2 machine, to obtain some statistical properties of the estimation method.
On moments of the supremum of normed weighted averages
Authors: Li Deli 
Published: June 2006

AbstractLet {X, Xn; n ≥ 1} be a sequence of real‐valued iid random variables, 0 < r < 2 and p > 0. Let D = {A = (ank; 1 ≤ k ≤ n, n ≥ 1); ank, ϵ R and supn, k |an,k| < ∞}. Set Sn(A) = ∑nk=1an, kXk for A ϵ D and n ≥ 1. This paper is devoted to determining conditions whereby E{supn ≥ 1, |Sn(A)|/n1/r}p < ∞ or E{supn ≥ 2 |Sn(A)|/2n log n)1/2}p < ∞ for every A ϵ D. This generalizes some earlier results, including those of Burkholder (1962), Choi and Sung (1987), Davis (1971), Gut (1979), Klass (1974), Siegmund (1969) and Teicher (1971).
Isotonic estimation of the intensity of a nonhomogeneous Poisson process: The multiple realization setup
Authors: Zielinski Jan M. , Wolfson David B. , Nilakantan Lakshmi , Confavreux Christian 
Published: June 2006

AbstractIn the 1950s Brunk and Van Eeden each obtained maximum‐likelihood estimators of a finite product of probability density functions under partial or complete ordering of their parameters. Their results play an important role in the general theory of inference under order restrictions and lead to an isotonic estimator of the intensity of a nonhomogeneous Poisson process. Here an elementary derivation of the maximum likelihood estimator (m.l.e.) for the intensity of a nonhomogeneous Poisson process is given when several (possibly censored) realizations are available. Boswell obtained the m.l.e. based on a single realization as well as a conditional m.l.e. under the same conditions. An example is given to show that in the multirealization setup a conditional m.l.e. may not exist; the proofs are, we believe, new and elementary. An illustrative application is given.
A characterization of probability distributions similar to the exponential
Authors: Chukova S. , Dimitrov B. , Khalil Z. 
Published: June 2006

AbstractA concept of the lack‐of‐memory property at a given time point c > 0 is introduced. It is equivalent to the concept of the almost‐lack‐of‐memory (ALM) property of the random variables. A representation theorem is given for the cumulative distribution function of such random variables as well as for corresponding decompositions in terms of independent random variables. It is shown that a periodic failure rate for a random variable is equivalent to the ALM property. In addition some properties of the service time of an unreliable server are observed.
Random r‐content of an r‐simplex from beta‐type‐2 random points
Authors: Chu David P.T. 
Published: June 2006

AbstractWe study the r‐content Δ of the r ‐simplex generated by r+ 1 independent random points in R”. Each random point Zj is isotropic and distributed according to λ||Zj||2 ∼ beta‐type‐2(n/2, v), λ, v > 0. We provide an asymptotic normality result which is analogous to the conjecture made by Miles (1971). A method is introduced to work out the exact density of W = (rλ)r(r!Δ)2/(r + |)r+l and hence that of Δ. The distribution of W is also related to some hypothesis‐testing problems in multivariate analysis. Furthermore, by using this method, the distribution of W or Δ can easily be simulated.
Some statistical opportunities in the agricultural sciences
Authors: Billard L. 
Published: June 2006

AbstractQuantitative plant‐disease epidemiology gained its primary motivation with the publication of VanderPlank's (1963) mathematical treatise, since which a range of predominantly deterministic models has been proposed by plant pathologists to describe the temporal dynamic of plant diseases. In this article, a number of results are reviewed. In particular, a summary is made of the primary models developed in the literature together with diseases for which they were proposed; estimation of the infection rate is considered for the generalized logistic model; and a bivariate stochastic model for stem and leaf infection is developed and used to obtain measures of plant disease stress.
Convergence results for strict C‐sequences
Authors: Bouzar Nadjib 
Published: June 2006

AbstractWe examine properties of strict C‐sequences. These sequences are a generalization both of eventual martingales and of quasimartingales. Several global and local convergence results are proved. The transform and the quadratic variation of a strict C‐sequence are also studied. A comparison with various martingale generalizations is established.
Small area estimation using unmatched sampling and linking models
Authors: You Yong , Rao J. N. K. 
Published: June 2006

AbstractThe authors use a hierarchical Bayes approach to area level unmatched sampling and Unking models for small area estimation. Empirically they compare inferences under unmatched models with those obtained under the customary matched sampling and linking models. They apply the proposed method to Canadian census undercoverage estimation, developing a full hierarchical Bayes approach using Markov Chain Monte Carlo sampling methods. They show that the method can provide efficient model‐based estimates. They use posterior predictive distributions to assess model fit.
Bayesian estimation of cognitive decline in patients with alzheimer's disease
Authors: Béalisle Patrick , Joseph Lawrence , Wolfson David B. , Zhou Xiaojie 
Published: June 2006

AbstractRecently, there has been great interest in estimating the decline in cognitive ability in patients with Alzheimer's disease. Measuring decline is not straightforward, since one must consider the choice of scale to measure cognitive ability, possible floor and ceiling effects, between‐patient variability, and the unobserved age of onset. The authors demonstrate how to account for the above features by modeling decline in scores on the Mini‐Mental State Exam in two different data sets. To this end, they use hierarchical Bayesian models with change points, for which posterior distributions are calculated using the Gibbs sampler. They make comparisons between several such models using both prior and posterior Bayes factors, and compare the results from the models suggested by these two model selection criteria.
Estimation of the mean of a spherically symmetric distribution with constraints on the norm
Authors: Fourdrinier Dominique , Ouassou Idir 
Published: June 2006

AbstractThe authors consider the problem of estimating, under quadratic loss, the mean of a spherically symmetric distribution when its norm is supposed to be known and when a residual vector is available. They give a necessary and sufficient condition for the optimal James‐Stein estimator to dominate the usual estimator. Various examples are given that are not necessarily variance mixtures of normal distributions. Consideration is also given to an alternative class of robust James‐Stein type estimators that take into account the residual vector. A more general domination condition is given for this class.
Nonparametric smoothing using state space techniques
Authors: Brown Patrick E. , De Jong Piet 
Published: June 2006

AbstractThe authors examine the equivalence between penalized least squares and state space smoothing using random vectors with infinite variance. They show that despite infinite variance, many time series techniques for estimation, significance testing, and diagnostics can be used. The Kalman filter can be used to fit penalized least squares models, computing the smoothed quantities and related values. Infinite variance is equivalent to differencing to stationarity, and to adding explanatory variables. The authors examine constructs called “smoothations” which they show to be fundamental in smoothing. Applications illustrate concepts and methods.
Dimensionality reduction approach to multivariate prediction
Authors: Merola Giovanni M. , Abraham Bovas 
Published: June 2006

AbstractThe authors consider dimensionality reduction methods used for prediction, such as reduced rank regression, principal component regression and partial least squares. They show how it is possible to obtain intermediate solutions by estimating simultaneously the latent variables for the predictors and for the responses. They obtain a continuum of solutions that goes from reduced rank regression to principal component regression via maximum likelihood and least squares estimation. Different solutions are compared using simulated and real data.
Diagnostic tests for bias of estimating equations in weighted regression with missing covariates
Authors: Claire Lei S.‐Y. , Wang Suojin 
Published: June 2006

AbstractThe authors propose two tests, one parametric and the other semiparametric, for testing bias of estimating equations in weighted regression with partially missing covariates when the primary regression model is correctly specified. More generally, the proposed tests may be thought of as a diagnostic tool for the combined package of the primary regression model and the missingness assumptions. The asymptotic null distributions of the two test statistics are derived under the assumption of missingness at random for the partially missing covariates. A small scale simulation study completes the work.
Goodness‐of‐fit tests for the hyperbolic distribution
Authors: Puig Pedro , Stephens Michael A. 
Published: June 2006

AbstractThe authors give tests of fit for the hyperbolic distribution, based on the Cramér‐von Mises statistic W2. They consider the general case with four parameters unknown, and some specific cases where one or two parameters are fixed. They give two examples using stock price data.
Nonparametric estimation of the bivariate cdf for arbitrarily censored data
Authors: Gentleman Robert , Vandal Alain C. 
Published: June 2006

AbstractRight, left or interval censored multivariate data can be represented by an intersection graph. Focussing on the bivariate case, the authors relate the structure of such an intersection graph to the support of the nonparametric maximum likelihood estimate (NPMLE) of the cumulative distribution function (CDF) for such data. They distinguish two types of non‐uniqueness of the NPMLE: representational, arising when the likelihood is unaffected by the distribution of the estimated probability mass within regions, and mixture, arising when the masses themselves are not unique. The authors provide a brief overview of estimation techniques and examine three data sets.
Testing lack of fit of regression models under heteroscedasticity
Authors: Li Chin‐Shang 
Published: June 2006

AbstractA test is proposed for assessing the lack of fit of heteroscedastic nonlinear regression models that is based on comparison of nonparametric kernel and parametric fits. A data‐driven method is proposed for bandwidth selection using the asymptotically optimal bandwidth of the parametric null model which leads to a test that has a limiting normal distribution under the null hypothesis and is consistent against any fixed alternative. The resulting test is applied to the problem of testing the lack of fit of a generalized linear model.
Bayesian variable selection for proportional hazards models
Authors: Ibrahim Joseph G. , Chen Ming‐Hui , MacEachern Steven N. 
Published: June 2006

AbstractThe authors consider the problem of Bayesian variable selection for proportional hazards regression models with right censored data. They propose a semi‐parametric approach in which a nonparametric prior is specified for the baseline hazard rate and a fully parametric prior is specified for the regression coefficients. For the baseline hazard, they use a discrete gamma process prior, and for the regression coefficients and the model space, they propose a semi‐automatic parametric informative prior specification that focuses on the observables rather than the parameters. To implement the methodology, they propose a Markov chain Monte Carlo method to compute the posterior model probabilities. Examples using simulated and real data are given to demonstrate the methodology.