基于Bootstrap方法的密封寿命可靠性评估

针对高可靠、长寿命密封件在小样本试验下的可靠性寿命评估需求,提出一种基于虚拟增广样本和Bootstrap方法的密封寿命小样本数据可靠性评估方法。并以轴用阶梯圈为例,进行寿命可靠性评估。首先通过虚拟增广法将轴用阶梯圈台架寿命试验数据样本数增广至10个,使得样本数满足Bootstrap方法的适用条件;然后再利用Bootstrap方法得到轴用阶梯圈的寿命可靠性评估结果。     

Structural and operational management of Turkish airports: a bootstrap data envelopment analysis of efficiency

This paper explores the structural and operational dimensions of the efficiencies of airports. The two-stage procedure is suggested to assess the efficiencies of airports in this study. In the first-stage, Classification and Regression Tree, which is one of the machine-learning approaches used to divide the airports into homogeneous and thus comparable sub-groups. In the second stage, the bootstrap data envelopment analysis approach obtains more precise structural and operational efficiency scores. To illustrate the proposed framework use, we applied it to a real case associated with Turkish airports. The results demonstrate that this framework presents a more comprehensive assessment of airport performance rather than conventional data envelopment analysis models. Moreover, it provides to show the deficiencies of the structural and operational management of airports. The findings can help anywhere airport authorities as well as Turkish airport authorities.     

Wind of change: Small-scale electricity production and distribution-grid efficiency in Sweden

In this paper, we measure the technical efficiency for local electricity distribution firms in Sweden, and in particular how small and micro-scale generation affects efficiency scores. Using a two-stage data envelopment analysis to model the technical efficiency and a double bootstrap approach to estimate the determinants of inefficiencies, we show that firms are heterogeneous in terms of inefficiency, but that a large share of small and micro-scale generation is not associated with more inefficient operations.     

Nonlinear Factor-Augmented Predictive Regression Models with Functional Coefficients

This article introduces a new class of functional-coefficient predictive regression models, where the regressors consist of auto-regressors and latent factor regressors, and the coefficients vary with certain index variable. The unobservable factor regressors are estimated through imposing an approximate factor model on high dimensional exogenous variables and subsequently implementing the classical principal component analysis. With the estimated factor regressors, a local linear smoothing method is used to estimate the coefficient functions (with appropriate rotation) and obtain a one-step ahead nonlinear forecast of the response variable, and then a wild bootstrap procedure is introduced to construct the prediction interval. Under regularity conditions, the asymptotic properties of the proposed methods are derived, showing that the local linear estimator and the nonlinear forecast using the estimated factor regressors are asymptotically equivalent to those using the true latent factor regressors. The developed model and methodology are further generalized to the factor-augmented vector predictive regression with functional coefficients. Finally, some extensive simulation studies and an empirical application to forecast the UK inflation are given to examine the finite-sample performance of the proposed model and methodology.     

A new MM algorithm for constrained estimation in the proportional hazards model

The constrained estimation in Cox’s model for the right-censored survival data is studied and the asymptotic properties of the constrained estimators are derived by using the Lagrangian method based on Karush–Kuhn–Tucker conditions. A novel minorization–maximization (MM) algorithm is developed for calculating the maximum likelihood estimates of the regression coefficients subject to box or linear inequality restrictions in the proportional hazards model. The first M-step of the proposed MM algorithm is to construct a surrogate function with a diagonal Hessian matrix, which can be reached by utilizing the convexity of the exponential function and the negative logarithm function. The second M-step is to maximize the surrogate function with a diagonal Hessian matrix subject to box constraints, which is equivalent to separately maximizing several one-dimensional concave functions with a lower bound and an upper bound constraint, resulting in an explicit solution via a median function. The ascent property of the proposed MM algorithm under constraints is theoretically justified. Standard error estimation is also presented via a non-parametric bootstrap approach. Simulation studies are performed to compare the estimations with and without constraints. Two real data sets are used to illustrate the proposed methods.     

有限样本下煤硫分典型值的自举模拟取样估计

在对无烟煤硫分基础统计学特征分析的基础上，采用内核密度估计对硫分数据多态性进行了研究，根据非正态分布的特点，使用Bootstrap计算机模拟取样方法对试验样本值模拟重复取样，以多次Bootstrap模拟取样的均值与标准误差作为无烟煤硫分有限样本典型值及标准误差的稳健估计，实践证明，Bootstrap模拟取样估计对无烟煤硫分典型值的估计是有效的.     

Bootstrap interval with application in environmental monitoring

ABSTRACT

Environmental research data usually present high variability because of the presence of outlier values. This study sought to apply the interval bootstrap method, as compared to Chebyshev’s Theorem, for the statistical assessment of environmental data with anomalous values. The results obtained showed that: (a) the bootstrap method is an excellent alternative in the assessment of environmental data with outliers; (b) the method percentile bootstrap (Boot-perc-s) presented the lower interval amplitude to the concentrations of ²²⁶Ra, and (c) Chebyshev’s Theorem presented the highest interval amplitude for the concentration of ²²⁶Ra.     

200 MHz带宽基带信号发生器的设计与实现

介绍了基于FPGA的基带信号发生器设计方案,主要叙述了成型滤波以及任意重采样内插比等关键技术。该基带信号发生器不仅实现了标准数字调制格式的基带信号发生,而且通过FPGA内部的并行架构设计,实现了500 MHz的高速数据速率,I/Q带宽达到200 MHz。将其应用于矢量信号发生器中,既实现了PSK、ASK、FSK、MSK、QAM等标准数字调制格式的矢量信号,又实现了400 MHz矢量调制带宽。经过测试,在4 M码元速率的情况下,QPSK和512QAM调制格式的EVM分别达到0.7%和1.0%;400 MHz带宽的矢量调制信号频响小于1 dB。     

A GENERALIZED BLOCK BOOTSTRAP FOR SEASONAL TIME SERIES

When time‐series data contain a periodic/seasonal component, the usual block bootstrap procedures are not directly applicable. We propose a modification of the block bootstrap – the generalized seasonal block bootstrap (GSBB) – and show its asymptotic consistency without undue restrictions on the relative size of the period and block size. Notably, it is exactly such restrictions that limit the applicability of other proposals of block bootstrap methods for time series with periodicities. The finite‐sample performance of the GSBB is also illustrated by means of a small simulation experiment.