Bootstrap procedures for detecting multiple persistence shifts in heteroskedastic time series

This article proposes new bootstrap procedures for detecting multiple persistence shifts in a time series driven by non-stationary volatility. The assumed volatility process can accommodate discrete breaks, smooth transition variation as well as trending volatility. We develop wild bootstrap sup-Wald tests of the null hypothesis that the process is either stationary [I(0)] or has a unit root [I(1)] throughout the sample. We also propose a sequential procedure to estimate the number of persistence breaks based on ordering the regime-specific bootstrap p-values. The asymptotic validity of the advocated procedures is established both under the null of stability and a variety of persistence change alternatives. A comparison with existing tests that assume homoskedasticity illustrates the finite sample improvements offered by our methods. An application to OECD inflation rates highlights the empirical relevance of the proposed approach and weakens the case for persistence change relative to existing procedures.     

Combining Cumulative Sum Change‐Point Detection Tests for Assessing the Stationarity of Univariate Time Series

We derive tests of stationarity for univariate time series by combining change‐point tests sensitive to changes in the contemporary distribution with tests sensitive to changes in the serial dependence. The proposed approach relies on a general procedure for combining dependent tests based on resampling. After proving the asymptotic validity of the combining procedure under the conjunction of null hypotheses and investigating its consistency, we study rank‐based tests of stationarity by combining cumulative sum change‐point tests based on the contemporary empirical distribution function and on the empirical autocopula at a given lag. Extensions based on tests solely focusing on second‐order characteristics are proposed next. The finite‐sample behaviors of all the derived statistical procedures for assessing stationarity are investigated in large‐scale Monte Carlo experiments, and illustrations on two real datasets are provided. Extensions to multi‐variate time series are briefly discussed as well.     

Assessing genomic prediction accuracy for Holstein sires using bootstrap aggregation sampling and leave-one-out cross validation

Since the introduction of genome-enabled prediction for dairy cattle in 2009, genomic selection has markedly changed many aspects of the dairy genetics industry and enhanced the rate of response to selection for most economically important traits. Young dairy bulls are genotyped to obtain their genomic predicted transmitting ability (GPTA) and reliability (REL) values. These GPTA are a main factor in most purchasing, marketing, and culling decisions until bulls reach 5 yr of age and their milk-recorded offspring become available. At that time, daughter yield deviations (DYD) can be compared with the GPTA computed several years earlier. For most bulls, the DYD align well with the initial predictions. However, for some bulls, the difference between DYD and corresponding GPTA is quite large, and published REL are of limited value in identifying such bulls. A method of bootstrap aggregation sampling (bagging) using genomic BLUP (GBLUP) was applied to predict the GPTA of 2,963, 2,963, and 2,803 young Holstein bulls for protein yield, somatic cell score, and daughter pregnancy rate (DPR), respectively. For each trait, 50 bootstrap samples from a reference population comprising 2011 DYD of 8,610, 8,405, and 7,945 older Holstein bulls were used. Leave-one-out cross validation was also performed to assess prediction accuracy when removing specific bulls from the reference population. The main objectives of this study were (1) to assess the extent to which current REL values and alternative measures of variability, such as the bootstrap standard deviation (SD) of predictions, could detect bulls whose daughter performance deviates significantly from early genomic predictions, and (2) to identify factors associated with the reference population that inform about inaccurate genomic predictions. The SD of bootstrap predictions was a mildly useful metric for identifying bulls whose future daughter performance may deviate significantly from early GPTA for protein and DPR. Leave-one-out cross validation allowed us to identify groups of reference population bulls that were influential on other reference population bulls for protein yield and observe their effects on predictions of testing set bulls, as a whole and individually.     

基于AT91SAM9263的嵌入式系统启动过程分析

以Atmel公司的AT91SAM9263为例分析研究了它的bootloader启动过程.对从系统上电后的第一条指令到加载Linux kernel的过程进行了分析,主要阐述了bootstrap和uboot的启动过程.掌握嵌入式开发的启动过程,对后续的开发至关重要.     

基于自举电路的开关磁阻电机初始位置估计方法

开关磁阻电机的转子必须与电机磁场保持同步才能保证电机的正常运行,因此对转子位置信息的准确检测非常重要。提出一种基于自举电路的开关磁阻电机初始位置估计方法,通过给自举电路中的电容充电,向各相绕组中注入诊断脉冲电流,根据各相诊断电流峰值和转子位置的关系,判断转子所在区间。该方法不依赖电机的具体电感模型,对控制系统带宽要求较低,无须电流闭环控制,检测过程不会造成电机转子的附加位移。阐述了利用自举电路检测开关磁阻电机初始位置的基本原理,给出了自举电路相关元件的选取原则,通过实验验证了所提出方法的有效性和实用性,并对引起误差的原因进行了分析。     

An ensemble wavelet bootstrap machine learning approach to water demand forecasting: a case study in the city of Calgary,Canada

This paper explores a hybrid wavelet, bootstrap and neural network (WBNN) modeling approach for daily (1, 3 and 5 day) urban water demand forecasting in situations with limited data availability. This method was tested using 3 years of daily water demand and meteorological data for the city of Calgary, Alberta, Canada. The performance of the WBNN method was compared to that of three other methods: traditional neural networks (NN), wavelet NNs (WNN), and bootstrap-based NN (BNN) models. While the hybrid WBNN and WNN models equally provided 1-day lead-time forecasts of greater accuracy than those obtained with other methods, for longer lead-time (3- or 5-day) forecasts the WBNN model alone outperformed the other models. The confidence bands generated using the WBNN model displayed the uncertainty associated with the forecasts.     

开关磁阻电机功率变换器驱动电路研究

基于自举式IR2110集成驱动芯片的驱动原理,设计了开关磁阻电机功率变换器驱动电路和相应的开关电源电路,不仅简化了逻辑电路对功率器件的控制要求,而且提高了驱动电路的可靠性,缩小控制板的尺寸。经仿真和实际使用,达到了设计要求。     

Bootstrap-based bandwidth choice for log-periodogram regression

Abstract.  The choice of the bandwidth in the local log-periodogram regression is of crucial importance for estimation of the memory parameter of a long memory time series. Different choices may give rise to completely different estimates, which may lead to contradictory conclusions, for example about the stationarity of the series. We propose here a data-driven bandwidth selection strategy that is based on minimizing a bootstrap approximation of the mean-squared error (MSE). Its behaviour is compared with other existing techniques for optimal bandwidth selection in a MSE sense, revealing its better performance in a wider class of models. The empirical applicability of the proposed strategy is shown with two examples: the widely analysed in a long memory context Nile river annual minimum levels and the input gas rate series of Box and Jenkins.     

Validity of sequential bootstrap for subcritical and critical branching processes

Sequential bootstrapping for branching processes with immigration is proposed. The asymptotic validity of a sequential bootstrap estimator of the offspring mean in is estabilished for all values of m ? (0, 1]. This result is to be constrasted with the standard (non-sequential)bootstrapping where the bootstrap estimator estimator has been shown to asymptotically invalid (see, Sriram (1992)).