Multinomial Probability Estimation by Wavelet Thresholding

In this article, we introduce a wavelet threshold estimator to estimate multinomial probabilities. The advantages of the estimator are its adaptability to the roughness and sparseness of the data. The asymptotic behavior of the estimator is investigated through an often-used criteria: the mean sum of squared error (MSSE). We show that the MSSE of the estimator achieves the optimal rate of convergence. Its performance on finite samples is examined through simulation studies which show favorable results for the new estimator over the commonly used kernel estimator.     

3D dynamic forward modeling of polar plumes using Hidden Markov Trees

The polar plumes are very fine structures of the solar K-corona lying around the poles and visible during the period of minimum of activity. These poorly known structures are linked with the solar magnetic field and with numerous coronal phenomena such as the fast solar wind and the coronal holes. The SOHO space mission has provided some continuous observations to high cadence (each 10 min). From these observations the images of the K-corona have been derived and preprocessed with an adapted anisotropic filtering. Then, a peculiar type of sinogram called Time Intensity Diagram (TID) has been built. It is adapted to the evolution of polar plumes with the time. A multiresolution wavelet analysis of the TID has then revealed that the spatial distribution of the polar plumes as well as their temporal evolution were fractal. The present study consists in simulating polar plumes by forward modeling techniques in order to validate several assumptions concerning their nature and their temporal evolution. Our work involves two main steps. The first one concerns the simulation of polar plumes and the computation of their corresponding TID. The second one concerns the estimation of analysis criteria in order to compare the original TID and the simulated ones. Static and dynamic models were both used in order to confirm the fractal behavior of the temporal evolution of plumes. The most recent and promising model is based on a Hidden Markov Tree. It allows us to control the fractal parameters of the TID.     

小波变换用于示波计时电位信号处理的研究

本文根据小波交换所具有的时频局域化、分频和时移不变性等性质,将其用于未波计时电位信号平滑与去噪、重叠峰分辨、有用信息提取多方面的研究．结果表明：小波交换是一种优良的信号处理技术,可以明显改善示波分析的现状．     

Denoising radiocommunications signals by using iterative wavelet shrinkage

Summary. Radiocommunications signals pose particular problems in the context of statistical signal processing. This is because short-term fluctuations (noise) are a consequence of atmospheric effects whose characteristics vary in both the short and the longer term. We contrast traditional time domain and frequency domain filters with wavelet methods. We also propose an iterative wavelet procedure which appears to provide benefits over existing wavelet methods.     

国际石油价格与通货膨胀的周期波动关系

近年来国际石油价格大幅波动,不可避免地给各国经济带来冲击和影响。本文利用小波变换频带分析方法,结合VAR和多元GARCH-BEKK模型,考察了不同经济周期下中美两国通货膨胀与国际油价间的溢出关系。结果表明,国际油价只在短周期里对中国通货膨胀有显著的单向均值溢出效应,而二者之间不存在任何方向的波动溢出效应;美国通货膨胀与国际油价则在多数周期下具有显著的单向或双向均值波动溢出效应。本文认为尽管当前中国通货膨胀与国际油价的关系并不显著,但随着我国石油消费对进口依赖的不断提高,能源安全问题将成为未来中国需要应对挑战,因此相关部门应及早采取有效措施,加快完善国内石油市场化建设,增强能源意识,提高能源利用效率,以应对未来石油冲击对经济的影响。     

Detection of Change Point in Nonparametric Function with Unit-Root Noise by Wavelet

A wavelet method is proposed to detect jumps in a function which is observed with unit-root noise. We obtain critical values at any scale and prove the consistency of wavelet detection when the nonparametric function is smooth. It shows that the estimation of the number and locations of change points are consistent when there are change points in the nonparametric function. Simulation study supports our method.     

A Wavelet-Based Approach of Testing for Granger Causality in the Presence of GARCH Effects

The size and power of the most commonly used tests and a new wavelet-based approach of testing for Granger causality is evaluated by means of a Monte Carlo study in which the error term follows a generalized autoregressive conditional heteroscedasticity consistent (GARCH) process. In the simulation study it is shown that the commonly used causality tests tend to overreject the true null hypothesis in the presence of GARCH errors and that the new wavelet-based approach improves the size properties of the Granger causality test for all of the different situations evaluated.     

Wavelet-Based Density Estimation in a Heteroscedastic Convolution Model

We consider a heteroscedastic convolution density model under the “ordinary smooth assumption.” We introduce a new adaptive wavelet estimator based on term-by-term hard thresholding rule. Its asymptotic properties are explored via the minimax approach under the mean integrated squared error over Besov balls. We prove that our estimator attains near optimal rates of convergence (lower bounds are determined). Simulation results are reported to support our theoretical findings.     

Wavelet estimation in time-varying coefficient time series models with measurement errors

The article studies a time-varying coefficient time series model in which some of the covariates are measured with additive errors. In order to overcome the bias of estimator of the coefficient functions when measurement errors are ignored, we propose a modified least squares estimator based on wavelet procedures. The advantage of the wavelet method is to avoid the restrictive smoothness requirement for varying-coefficient functions of the traditional smoothing approaches, such as kernel and local polynomial methods. The asymptotic properties of the proposed wavelet estimators are established under the α-mixing conditions and without specifying the error distribution. These results can be used to make asymptotically valid statistical inference.     

A non linear wavelet based estimator for long memory processes

Two wavelet based estimators are considered in this paper for the two parameters that characterize long range dependence processes. The first one is linear and is based on the statistical properties of the coefficients of a discrete wavelet transform of long range dependence processes. The estimator consists in measuring the slope (related to the long memory parameter) and the intercept (related to the variance of the process) of a linear regression after a discrete wavelet transform is performed (Veitch and Abry, 1999). In this paper its properties are reviewed, and analytic evidence is produced that the linear estimator is applicable only when the second parameter is unknown. To overcome this limitation a non linear wavelet based estimator - that takes into account that the intercept depends on the long memory parameter - is proposed here for the cases in which the second parameter is known or the only parameter of interest is the long memory parameter. Under the same hypothesis assumed for the linear estimator, the non linear estimator is shown to be asymptotically more efficient for the long memory parameter. Numerical simulations show that, even for small data sets, the bias is very small and the variance close to optimal. An application to ATM based Internet traffic is presented.Financial support from the Italian Ministry of University and Scientific Research (MIUR), also in the context of the COFIN 2002 ALINWEB (Algorithms for the Internet and the Web) Project, is gratefully acknowledged.