小波分析在管理数据处理中的应用

提出了一种基于小波变换的管理数据处理方法，把公司管理上的数据看成一个非平稳的时间序列，利用小渡函数将该时间序列分解到不同的频率通道上，然后将分解后的信号当作近似的平稳时间序列，用一些传统的统计方法进行预测，同时对中国足球彩票若干期的销售量数据进行了处理和预测，并将结果与实际销量以及用传统的AR模型的预测值进行了比较。     

GPS接收机P(Y)直捕方法研究

GPS接收机P(Y)直捕算法是基于存储的滑动相关搜索,并利用FFT将时频二维联合搜索变成时域一维搜索。同时,考虑位同步点已知的条件,接收资源池只需存储一段接收序列,缩短了捕获时间且利于硬件实现。仿真结果表明,多普勒频移为±5kHz时,所带来的峰值衰减为2dB左右,本地信号与接收信号对齐时出现明显的相关峰,很好地实现了捕获功能;提出了用重叠分段补零FFT操作实现并行相关,成倍降低了捕获时间;提出了利用位同步信息,避免了码极性翻转可能引起的信噪比损耗;针对码相位漂移,提出了优化捕获策略,可在短时间内完成重捕。     

Fast retraction of evidence in a probabilistic expert system

We present and justify a propagation algorithm to facilitate the simultaneous calculation, for every node in a probabilistic exper system of the distribution of the associated random quantity, conditional on all the evidence obtained about the remaining nodes.     

浅谈如何提高大学生英语阅读理解能力兼评《全新版大学英语快速阅读》

阅读理解能力是衡量一个人英语水平的重要因素之一,随着社会的发展和大学英语教学改革的大力推进,我们应当进一步加强对大学生阅读理解能力的培养,扩充他们对词汇量的掌握,并使他们了解相关的文化背景,从而达到提高阅读速度的目的。     

北魏道士寇谦之的新道教论析

北魏道士寇谦之是新道教的倡行者,是民间道教转化为官方道教的重要人物。寇谦之的著述及《老君音诵诫经》所反映的思想,说明寇谦之秉承早期正一道教诫,新道教以维护张陵教法为特点。寇谦之汲取儒释思想于新科之中,其改革为道教成为国家宗教奠定了基础。     

Real nonparametric regression using complex wavelets

Summary.  Wavelet shrinkage is an effective nonparametric regression technique, especially when the underlying curve has irregular features such as spikes or discontinuities. The basic idea is simple: take the discrete wavelet transform of data consisting of a signal corrupted by noise; shrink or remove the wavelet coefficients to remove the noise; then invert the discrete wavelet transform to form an estimate of the true underlying curve. Various researchers have proposed increasingly sophisticated methods of doing this by using real-valued wavelets. Complex-valued wavelets exist but are rarely used. We propose two new complex-valued wavelet shrinkage techniques: one based on multiwavelet style shrinkage and the other using Bayesian methods. Extensive simulations show that our methods almost always give significantly more accurate estimates than methods based on real-valued wavelets. Further, our multiwavelet style shrinkage method is both simpler and dramatically faster than its competitors. To understand the excellent performance of this method we present a new risk bound on its hard thresholded coefficients.     

基于视觉模型的双正交小波图像水印算法

提出了一种基于人类视觉系统模型的双正交小波多分辨图像水印算法,该算法充分利用小波多分辨的优点及人眼视觉系统的特性,保证了水印的不可见性及鲁棒性,同时在检测水印时也不需要原始图像,仅需涉及到相关的几个细节子图。实验表明该水印算法对于中值滤波、叠加加性噪声、JPEG有损压缩三种图像处理都是鲁棒的。检测算法还有计算量小,检测速度快的特点。     

Shrinkage confidence intervals for the normal mean: Using a guess for greater efficiency

If the unknown mean of a univariate population is sufficiently close to the value of an initial guess then an appropriate shrinkage estimator has smaller average squared error than the sample mean. This principle has been known for some time, but it does not appear to have found extension to problems of interval estimation. The author presents valid two‐sided 95% and 99% “shrinkage” confidence intervals for the mean of a normal distribution. These intervals are narrower than the usual interval based on the Student distribution when the population mean lies in such an “effective interval.” A reduction of 20% in the mean width of the interval is possible when the population mean is sufficiently close to the value of the guess. The author also describes a modification to existing shrinkage point estimators of the general univariate mean that enables the effective interval to be enlarged.     

Generalised Rank Regression Estimator with Standard Error Adjusted Lasso

One of the standard variable selection procedures in multiple linear regression is to use a penalisation technique in least‐squares (LS) analysis. In this setting, many different types of penalties have been introduced to achieve variable selection. It is well known that LS analysis is sensitive to outliers, and consequently outliers can present serious problems for the classical variable selection procedures. Since rank‐based procedures have desirable robustness properties compared to LS procedures, we propose a rank‐based adaptive lasso‐type penalised regression estimator and a corresponding variable selection procedure for linear regression models. The proposed estimator and variable selection procedure are robust against outliers in both response and predictor space. Furthermore, since rank regression can yield unstable estimators in the presence of multicollinearity, in order to provide inference that is robust against multicollinearity, we adjust the penalty term in the adaptive lasso function by incorporating the standard errors of the rank estimator. The theoretical properties of the proposed procedures are established and their performances are investigated by means of simulations. Finally, the estimator and variable selection procedure are applied to the Plasma Beta‐Carotene Level data set.     

Bayes shrinkage estimator for consistency assessment of treatment effects in multi-regional clinical trials

The primary objective of a multi-regional clinical trial is to investigate the overall efficacy of the drug across regions and evaluate the possibility of applying the overall trial result to some specific region. A challenge arises when there is not enough regional sample size. We focus on the problem of evaluating applicability of a drug to a specific region of interest under the criterion of preserving a certain proportion of the overall treatment effect in the region. We propose a variant of James-Stein shrinkage estimator in the empirical Bayes context for the region-specific treatment effect. The estimator has the features of accommodating the between-region variation and finiteness correction of bias. We also propose a truncated version of the proposed shrinkage estimator to further protect risk in the presence of extreme value of regional treatment effect. Based on the proposed estimator, we provide the consistency assessment criterion and sample size calculation for the region of interest. Simulations are conducted to demonstrate the performance of the proposed estimators in comparison with some existing methods. A hypothetical example is presented to illustrate the application of the proposed method.