A non-parametric statistic for testing conditional heteroscedasticity for unobserved component models

When prediction intervals are constructed using unobserved component models (UCM), problems can arise due to the possible existence of components that may or may not be conditionally heteroscedastic. Accurate coverage depends on correctly identifying the source of the heteroscedasticity. Different proposals for testing heteroscedasticity have been applied to UCM; however, in most cases, these procedures are unable to identify the heteroscedastic component correctly. The main issue is that test statistics are affected by the presence of serial correlation, causing the distribution of the statistic under conditional homoscedasticity to remain unknown. We propose a nonparametric statistic for testing heteroscedasticity based on the well-known Wilcoxon''s rank statistic. We study the asymptotic validation of the statistic and examine bootstrap procedures for approximating its finite sample distribution. Simulation results show an improvement in the size of the homoscedasticity tests and a power that is clearly comparable with the best alternative in the literature. We also apply the test on real inflation data. Looking for the presence of a conditionally heteroscedastic effect on the error terms, we arrive at conclusions that almost all cases are different than those given by the alternative test statistics presented in the literature.     

LC滤波器设计的优化解析公式

给出了改进的Chebyshev和Butterworth低通原型LC滤波器的解析设计公式.此公式充分利用阶数取整带来的余量,在不改变滤波器阶数的前提下减小了通带波动,极大地方便了集成滤波器的实现.给出了集成有源高通滤波器设计实例,验证了改进的Chebyshev和Butterworth低通原型LC滤波器解析设计公式的有效性.     

两个不同品牌洗衣粉抑菌效果的测定与研究

为了比较汰渍、碧浪两种不同品牌洗衣粉的抑菌效果,本试验采用滤纸片琼脂平板扩散法,将待洲样品溶液转移至接种有大肠杆菌(Escherichia coli)、枯草芽孢杆菌(Bacillus Subtilis)和金黄色葡萄球菌(Staphylococcus aureus)敏感菌株的培养基上,经培养后测定其抑菌圈的大小,以判断其抑菌能力的强弱.通过对抑菌圈直径的比较和两个均数的差异显著性分析,发现汰溃、碧浪两种洗衣粉的押菌效果差异不显著.     

美国互联网过滤制度研究

美国互联网过滤制度是指美国联邦政府以扣减联邦补贴作为强制手段,要求公立中小学、博物馆和图书馆过滤互联网上对未成年人有害信息的法律制度。透过相关法律的制定过程、概念、特征、主要措施、落实情况,以及治理模式及其背后的政治、经济、社会和技术原因,提出四点启示：遵循互联网信息传播规律,采用间接监管和共同治理方式,减少对企业的限制和负担,减少对成年人的妨碍等。     

气浮综合改进设施处理高浓度造纸废水

本文报道了气浮综合改进设施处理高浓度造纸废水的流程和改革，结果表明：改进后的气浮设施COD去除率由60％提高到75％，SS去除率由51％提高到71％，气浮出水河直接回用于造纸的主要过程，处理效果明显优于单纯的气浮处理。而且改进部分投资少，动力消耗少，是处理高浓度造纸废水的简便易行、值得推广的方法。     

Model-based measurement of latent risk in time series with applications

Summary.  Risk is at the centre of many policy decisions in companies, governments and other institutions. The risk of road fatalities concerns local governments in planning countermeasures, the risk and severity of counterparty default concerns bank risk managers daily and the risk of infection has actuarial and epidemiological consequences. However, risk cannot be observed directly and it usually varies over time. We introduce a general multivariate time series model for the analysis of risk based on latent processes for the exposure to an event, the risk of that event occurring and the severity of the event. Linear state space methods can be used for the statistical treatment of the model. The new framework is illustrated for time series of insurance claims, credit card purchases and road safety. It is shown that the general methodology can be effectively used in the assessment of risk.     

带通滤波器的计算新思路

用一种新的思路和方法 ,先计算低通、再计算高通滤波器的有关参数 ,然后组合成带通滤波器     

单片微机MCS-51实时测量系统中一批数据的一种数字滤波方法

提出了一种在单片微机实时测量系统中对成批数据进行二次算术平均数字滤波的一种方法，具有节省内存、编程简洁、运行较快的特点，滤波系数ｎ，ｍ和采样间隔ＴＳ通过键盘设置，是对在一定范围内波动变化的参量进行预处理的一种较好方法     

宽带数字下变频的高效实现方法研究

针对软件无线电接收机中数字下变频的特点,提出了一种合理的基于FPGA实现宽带数字下变频的方案,即分级实现以降低抗混叠滤波器的阶数;并且每级采用不同算法实现滤波抽取以占用不同资源,从而实现FPGA总体资源的合理、高效利用。另外,论述了一种适合FIR抽取滤波器的算法——时钟选择运算法,并通过Altera公司的EP2S60F484C4对该算法进行了测试,验证了它的高效性。     

Computational methods for complex stochastic systems: a review of some alternatives to MCMC

We consider analysis of complex stochastic models based upon partial information. MCMC and reversible jump MCMC are often the methods of choice for such problems, but in some situations they can be difficult to implement; and suffer from problems such as poor mixing, and the difficulty of diagnosing convergence. Here we review three alternatives to MCMC methods: importance sampling, the forward-backward algorithm, and sequential Monte Carlo (SMC). We discuss how to design good proposal densities for importance sampling, show some of the range of models for which the forward-backward algorithm can be applied, and show how resampling ideas from SMC can be used to improve the efficiency of the other two methods. We demonstrate these methods on a range of examples, including estimating the transition density of a diffusion and of a discrete-state continuous-time Markov chain; inferring structure in population genetics; and segmenting genetic divergence data.