Bootstrap Approximation to Distributions of Finite Population U-statistics

We show that the without replacement bootstrap of Booth, Butler and Hall (J. Am. Stat. Assoc. 89, 1282–1289, 1994) provides second order correct approximation to the distribution function of a Studentized U-statistic based on simple random sample drawn without replacement. In order to achieve similar approximation accuracy for the bootstrap procedure due to Bickel and Freedman (Ann. Stat. 12, 470–482, 1984) and Chao and Lo (Sankhya Ser. A 47, 399–405, 1985) we introduce randomized adjustments to the resampling fraction.      

Weighted bootstrap for U-statistics

In this paper we investigate the weighted bootstrap for U-statistics and its properties. Under very general choices of random weights and certain regularity conditions, we show that the weighted bootstrap method with U-statistics provides second-order accurate approximations to the distribution of U-statistics. We shall prove this via one-term Edgeworth expansions of weighted U-statistics.     

Judicial productivity,delay and efficiency: A Directional Distance Function (DDF) approach

The aim of this paper is twofold. On the one hand, it provides a contribution to the debate on judicial efficiency by conducting an applied research on the Italian tax judiciary thanks to a database covering the activities of the Italian tax courts over a 3-year period (2009–2011). On the other hand, it also contributes to the methodological debate, as it compares results obtained with Data Envelopment Analysis (DEA) and Directional Distance Function (DDF), two related non-parametric techniques which allow evaluating the efficiency of each observation as the radial distance from the efficient frontier defined by the best observations. While DEA has already been used to assess the mere technical efficiency of judicial systems, the DDF offers a valuable additional contribution, since it makes it possible to minimize the social cost of production of adjudication in the measurement. This feature makes it particularly attractive in those sectors in which production externalities may arise, such as judicial delays in the case investigated here. Additionally, the paper first applies the bootstrap to the DDF procedure in order to provide more robust estimates and to compare them with the DEA results.     

Finite-Size Effects for Anisotropic Bootstrap Percolation: Logarithmic Corrections

In this note we analyse an anisotropic, two-dimensional bootstrap percolation model introduced by Gravner and Griffeath. We present upper and lower bounds on the finite-size effects. We discuss the similarities with the semi-oriented model introduced by Duarte.     

负相伴随机序列的bootstrap中心极限定理

对于负相伴（NA）的严平稳随机序列，证明在二阶矩存在的条件下，其Moving Block Bootstrap样本满足中心极限定理．     

快速反应过程中的非线性扩散方程组

本文在多维空间中讨论具有实际应用背景的一类非线性反应扩散方程组,该方程组允许具有一定的退化性.这类问题在含有抗体和病毒两种反应物的非线性扩散系统的研究中具有重要的意义.本文用Bootstrap方法研究这类方程组弱解的存在性和唯一性;同时讨论了系统在反应速度非常快时弱解的收敛性,并得出了极限函数之间的关系.     

随机PP Kolmogorov多维拟合优度检验乃其Bootstrap逼近

本文提出了一种基于随机选择投影方向的PP型棉球等高分布族的拟合优度检验，其特点是计算上较通常的PP检验统计量简单．得到了其检验统计量在零假设下的极限分布，讨论了其Bootstrap逼近及逼近的相容性．     

Bootstrap method and empirical process

In this paper we consider the sampling properties of the bootstrap process, that is, the empirical process obtained from a random sample of size n (with replacement) of a fixed sample of size n of a continuous distribution. The cumulants of the bootstrap process are given up to the order n ^–1 and their unbiased estimation is discussed. Furthermore, it is shown that the bootstrap process has an asymptotic minimax property for some class of distributions up to the order n ^–1/2.     

稳态托卡马克堆中自举电流定标律和驱动电流的一些讨论

在设定自举电流为托卡马克堆中固有电流的基础上，根据是否存在α粒子和驱动电流的不同情况，本文讨论了自举电流的定标律和维持不变的总电流及其分布所需的驱动电流之大小及分布。     

Inference for a progressive stress model from Weibull distribution under progressive type-II censoring

Based on progressively type-II censored samples, this paper considers progressive stress accelerated life tests when the lifetime of an item under use condition follows the Weibull distribution with a scale parameter satisfying the inverse power law. It is assumed that the progressive stress is directly proportional to time and the cumulative exposure model for the effect of changing stress holds. Point estimation of the model parameters is obtained graphically by using Weibull probability paper plot that serves as a tool for model identification and also by using the maximum likelihood method. Interval estimation is performed by finding approximate confidence intervals (CIs) for the parameters as well as the studentized-t and percentile bootstrap CIs. Monte Carlo simulation study is carried out to investigate the precision of the estimates and compare the performance of CIs obtained. Finally, two examples are presented to illustrate our results.