Bootstrapping Analysis of Lifetime Data with Subsampling

Because of cost and time limitations, reliability experiments frequently contain subsampling, which is a restriction on randomization. A two‐stage approach can analyze right censored Weibull distributed reliability data with subsampling. However, in implementing such a method, we found that it did not address the problems of how to perform confidence intervals of low percentiles and reduce the bias of estimates. In this paper, we present a two‐stage bootstrapping approach and an unbiasing factor approach to solve the aforementioned problems. An example is provided to illustrate the proposed method. In addition, the proposed method is compared with existing methods through simulation. The resulting simulations show that the proposed method performs well in low percentiles. Copyright © 2015 John Wiley & Sons, Ltd.     

EFFICIENT METHOD OF MOMENTS ESTIMATORS FOR INTEGER TIME SERIES MODELS

The parameters of integer autoregressive models with Poisson, or negative binomial innovations can be estimated by maximum likelihood where the prediction error decomposition, together with convolution methods, is used to write down the likelihood function. When a moving average component is introduced this is not the case. To address this problem an efficient method of moment estimator is proposed where the estimated standard errors for the parameters are obtained using subsampling methods. The small sample properties of the estimator are investigated using Monte Carlo methods, while the approach is demonstrated using two well‐known examples from the time series literature.     

On nonparametric prediction of linear processes

Abstract.  We consider nonparametric prediction problem for both short- and long-range-dependent linear processes. Asymptotic properties of local linear estimates are obtained and, for long-range-dependent processes, an interesting dichotomous phenomenon is described: the limiting distribution depends on the interplay between the strength of the dependence and the magnitude of the bandwidth. A simulation study is carried out to assess the performance of the nonparametric prediction estimator.     

JPEG标准格式的编码方法

简要介绍了基本的JPEG的编码方法，即从采样到DCT编码再到量化及最后用霍夫曼编码压缩这一过程。     

Assessing Time‐Reversibility Under Minimal Assumptions

Abstract. This article considers a simple procedure for assessing whether a weakly dependent univariate stochastic process is time‐reversible. Our approach is based on a simple index of the deviation from zero of the median of the one‐dimensional marginal law of differenced data. An attractive feature of the method is that it requires no moment assumptions. Instead of relying on Gaussian asymptotic approximations, we consider using subsampling and resampling methods to construct confidence intervals for the time‐reversibility parameter, and show that such inference procedures are asymptotically valid under a mild mixing condition. The small‐sample properties of the proposed procedures are examined by means of Monte Carlo experiments and an application to real‐world data is also presented.     

A −40-dB EVM 20-MHz subsampling multistandard receiver architecture with dynamic carrier detection,bandwidth estimation,and EVM optimization

In this work, a reconfigurable multistandard subsampling receiver with dynamic carrier frequency detection and system-level EVM optimizations is proposed. Ideal software defined radio (SDR) receivers promise complete flexibility at the expense of high-performance analog-to-digital converters (ADCs) that are challenging to implement in current technologies for low-power applications. This scenario leads to the research of digital intensive sampling receivers with discrete-time signal processing (DTSP) implemented in analog domain. This approach makes it feasible to move channel selection filtering and dynamic gain adaptability from analog to digital domain. The proposed receiver employs subsampling down-conversion along with subband filters to dynamically detect the carrier frequency of the incoming signal, estimate its bandwidth, and identify if the signal is present in one of the target standard bands. This carrier detection provides a unique capability to reconfigure the receiver dynamically. Additionally, in this work, system-level EVM optimization is proposed considering frequency synthesizer phase noise, IQ mismatch, sampling frequency selection and block-level gain, noise, and nonlinearity. The RF front end of the proposed receiver is modeled in Verilog-AMS whereas the digital signal processing is implemented in Simulink-Matlab. The complete receiver has been verified to detect and process three different bands belonging to three different standards (GSM, UMTS, and WLAN) with the carrier frequency ranging from 0.9 to 2.5 GHz. Test signals with 4-QAM modulation, maximum bandwidth of 20 MHz, and input-dynamic range from –109 to –20 dBm is utilized to demonstrate the receiver performance including an EVM of –40 dB.     

医学图像配准中的数据抽样方法研究

针对在基于灰度的医学图像配准中,传统数据抽样方法在过抽样时产生较多的局部极值点问题,提出了基于浮动图像灰度概率分布和其梯度信息的抽样方法.通过对人体脑部的刚体配准实验,从函数曲线和收敛性能方面,对比分析了五种数据抽样方法.实验结果表明,新抽样方法可以有效地减少局部极值点,提高归一化互信息测度的收敛性能.     

The Slow Convergence of Ordinary Least Squares Estimators of α, β and Portfolio Weights under Long‐Memory Stochastic Volatility

We consider inference for the market model coefficients based on simple linear regression under a long memory stochastic volatility generating mechanism for the returns. We obtain limit theorems for the ordinary least squares (OLS) estimators of α and β in this framework. These theorems imply that the convergence rate of the OLS estimators is typically slower than if both the regressor and the predictor have long memory in volatility, where T is the sample size. The traditional standard errors of the OLS‐estimated intercept () and slope (), which disregard long memory in volatility, are typically too optimistic, and therefore the traditional t‐statistic for testing, say, α = 0 or β = 1, will diverge under the null hypothesis. We also obtain limit theorems (which imply slow convergence) for the estimated weights of the minimum variance portfolio and the optimal portfolio in the same framework. In addition, we propose and study the performance of a subsampling‐based approach to hypothesis testing for α and β. We conclude by noting that analogous results hold under more general conditions on long‐memory volatility models and state these general conditions which cover certain fractionally integrated exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models.     

基于卷积稀疏组合算法的轴承性能衰减评估

为有效监控与评估轴承工作状态,提出一种基于卷积稀疏组合算法评估方案。基于卷积神经网络框架建立轴承性能稀疏表示判别准则,并预测轴承的性能衰减程度;利用轴承衰减自相关函数,预判与轴承谱相关的密度条件,并在分析其他模型数值参量的基础上,验证评估方法的应用平稳性。选取退化指标作为实验对象,并通过分析相关的指标参数值可知,提出算法的评估结果可解释性强,能够较好维护轴承性能的衰减机制,影响系数值被控制在[-1,1]之间;在与传统算法的预测性能对比中,提出算法在两种状态下的偏差值分别为0.02和0.01,优于传统的轴承性能评估算法,同时在评估预测效率方面也具有一定优势。     

Measuring nonlinear dependence in time‐series,a distance correlation approach

We extend the concept of distance correlation of Szekely et al. (2007) and propose the auto distance correlation function (ADCF) to measure the temporal dependence structure of time‐series. Unlike the classic measures of correlations such as the autocorrelation function, the proposed measure is zero if and only if the measured time‐series components are independent. In this article, we propose and theoretically verify a subsampling methodology for the inference of sample ADCF for dependent data. Our methodology provides a useful tool for exploring nonlinear dependence structures in time‐series.