bluuebunny/crossref_metadata_2025_split huggingface dataset:
The likelihood ratio under noncontiguous alternatives
Authors: Gombay Edit 
Published: June 2006

AbstractThe 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

AbstractFor 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

AbstractThis 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

AbstractThe 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

AbstractL'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

AbstractThe 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

AbstractThe 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

AbstractBayesian 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

AbstractThe 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

AbstractThe 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

AbstractThe 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

AbstractThe 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

AbstractSemiparametric 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

AbstractSeveral 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

AbstractWe 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

AbstractStatistics 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

AbstractFor 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

AbstractNous 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

AbstractRandomly 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

AbstractLet 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

AbstractThe 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

AbstractModel 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

AbstractThe 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.
VaporLIFT: On‐Chip Chemical Synthesis of Glycan Microarrays
Authors: Tsouka Alexandra , Mende Marco , Heidepriem Jasmin , Dallabernardina Pietro , Garcia Ricardo Manuel , Schmidt Tobias , Bienert Klaus , Seeberger Peter H. , Loeffler Felix F. 
Published: March 2024

AbstractLaser‐induced forward transfer (LIFT) of polymers is a versatile printing method for parallel in situ synthesis of peptides on microarrays. Chemical building blocks embedded in a polymer matrix are transferred and coupled in a desired pattern to a surface, generating peptides on microarrays by repetitive in situ solid‐phase synthesis steps. To date, the approach is limited to simple, heat induced chemical reactions. The VaporLIFT method, disclosed here, combines LIFT with chemical vapor glycosylation to rapidly generate glycans on microarray surfaces while maintaining inert, low temperature conditions required for glycosylations. Process design and parameter optimization enables the synthesis of a collection of glycans at defined positions on a glass surface. The synthetic structures are detected by mass spectrometry, fluorescently labeled glycan‐binding proteins, and covalent staining with fluorescent dyes. VaporLIFT is ideal for parallel screening of other chemical reactions, that require inert and well‐defined reaction conditions.
Enabling High Performance Bismuth Trifluoride Cathode by Engineering the Cathode/Electrolyte Interface in Sulfide‐Based All Solid State Batteries
Authors: Xiong Bin , Zhang Xuedong , Ou Xiangze , Ren Xiaolei , Chen Junyu , Rao Yaling , Wang Yi , Huang Jianyu , Huang Qiao 
Published: March 2024

AbstractMetal fluorides are conversion‐type cathodes that have the potential to boost the energy densities of next generation lithium‐ion batteries (LIBs). However, the study of non‐transitional metal fluorides (NTMFs) such as bismuth trifluoride (BiF3) is limited due to the challenges on the construction of a stable electrochemical reaction interfaces with liquid electrolyte, although it shows advantages on high electrochemical potential, moderately high theoretical capacity and low voltage hysteresis. Moreover, the performance of BiF3 in all solid state batteries (ASSBs) has not been explored. In this contribution, the micro‐sized commercial BiF3 is successfully coated with a cyclic polyacrylonitrile (cPAN) and refined its size to nanoscale. The refined nano‐sized BiF3@cPAN uniformly disperses in the solid electrode and delivers an initial discharge capacity of 330 and 200 mAh g−1 after 250 cycles in sulfide electrolyte based ASSBs. Furthermore, the voltage hysteresis of the ASSBs reaches a record low value of 180 mV. Postmortem analysis shows that the elastic coating hindered the undesirable interface side reaction and rendered the BiF3 with excellent cycle reversibility. This work demonstrates the crucial role of stable interfaces for BiF3 in preventing electrolyte decomposition, which promotes the practical adoption of BiF3 cathode with higher specific energy for LIBs.
Aligned Bamboo Fiber‐Induced Crystallinity Mitigation of Lightweight Polymer Composite Enables Ultrahigh Strength and Unprecedented Toughness
Authors: Hou Jingkun , Wu Xianzhang , Li Zhihan , Li Xingchi , Liao Yu , Li Lei , Luo Sha , Wu Yiqiang , Qing Yan 
Published: March 2024

AbstractLightweight polymer composites promise incredible applications in aerospace, seaprobes, and medical apparatus. However, their performance is generally limited by a trade‐off between mechanical strength and toughness. Herein, a crystallinity mitigating strategy driven by highly aligned bamboo macrofibers embedded in a polycaprolactone polyol (PCL) matrix for producing ultrastrong and tough lightweight polymer composites is proposed. The embedded bamboo macrofibers have oxygen‐containing functional groups on the fiber surface, that can interact with functional groups (ester and hydroxyl groups) in the molecular chains of the PCL in the form of hydrogen bonds, thus preventing the aggregation of molecular chains and the crystallization of PCL, which ultimately leads to unprecedented toughness. Meanwhile, the bamboo macrofibres with intrinsically aligned microstructure, can enable effective stress transfer and dissipation, providing remarkable ultrahigh strength. As a result, the obtained lightweight polymer composite achieves ultrahigh mechanical strength (31.5 MPa) and superior toughness (21.7 MJ m−3) at an unprecedented low density (1.07 g cm−3), representing the state‐of‐the‐art in reported lightweight polymers. Such lightweight polymer composite has the potential to greatly expedite the practical realization of artificial medical materials, including orthopedic instruments and joint prostheses.
BCM‐Inspired Synapses Constructed with Barrier‐Modulated Coupling Junctions for Enhancing Speech Recognition
Authors: Cai Dan , Liu Yunbo , Wang Jinyong , Zhao Tianchen , Shen Miao , Zhang Fangjie , Jiang Yadong , Gu Deen 
Published: March 2024

AbstractBio‐inspired synaptic devices have garnered considerable interest in neuromorphic computing. The Bienenstock‐Cooper‐Munro (BCM) learning rule stands out as one of the most accurate synaptic models, featuring non‐monotonic behavior and threshold sliding effect, crucial for stable learning processes. The direct device strategy for completely mimicking the BCM rule is a tough issue since the current devices lack two competitive working modes within one device. In this work, a dual‐junction synaptic device with opposite built‐in electric fields using a W/WO2/WO3‐x/Au structure is demonstrated. The devices directly mimic two fundamental features of the BCM rule via a delicately‐designed bandgap engineering strategy. Furthermore, the working mechanisms are investigated and the promising potential of dual‐junction synaptic devices is demonstrated for enhancing speech recognition through Convolutional Neural Network (CNN)‐based digital speech recognition with a remarkable accuracy of 98% through a synaptic array. Even for speech recognition with 13% Gaussian noise, the accuracy remained at 83%. These findings provide a promising strategy for developing BCM‐based synaptic devices for neuromorphic computing applications.