Model Selection for Broadband Semiparametric Estimation of Long Memory in Time Series

We study the properties of Mallows' C _L criterion for selecting a fractional exponential (FEXP) model for a Gaussian long-memory time series. The aim is to minimize the mean squared error of a corresponding regression estimator d _FEXP of the memory parameter, d . Under conditions which do not require that the data were actually generated by a FEXP model, it is known that the mean squared error MSE=E[ d _FEXP− d ]² can converge to zero as fast as (log n )/ n , where n is the sample size, assuming that the number of parameters grows slowly with n in a deterministic fashion. Here, we suppose that the number of parameters in the FEXP model is chosen so as to minimize a local version of C _L, restricted to frequencies in a neighborhood of zero. We show that, under appropriate conditions, the expected value of the local C _L is asymptotically equivalent to MSE. A combination of theoretical and simulation results give guidance as to the choice of the degree of locality in C _L.     

On Local Trigonometric Regression Under Dependence

We consider nonparametric estimation of an additive time series decomposition into a long‐term trend μ and a smoothly changing seasonal component S under general assumptions on the dependence structure of the residual process. The rate of convergence of local trigonometric regression estimators of S turns out to be unaffected by the dependence, even though the spectral density of the residual process has a pole at the origin. In contrast, the rate of convergence of nonparametric estimators of μ depends on the long‐memory parameter d. Therefore, in the presence of long‐range dependence, different bandwidths for estimating μ and S should be used. A data adaptive algorithm for optimal bandwidth choice is proposed. Simulations and data examples illustrate the results.     

A Structural‐Factor Approach to Modeling High‐Dimensional Time Series and Space‐Time Data

This article considers a structural‐factor approach to modeling high‐dimensional time series and space‐time data by decomposing individual series into trend, seasonal, and irregular components. For ease in analyzing many time series, we employ a time polynomial for the trend, a linear combination of trigonometric series for the seasonal component, and a new factor model for the irregular components. The new factor model simplifies the modeling process and achieves parsimony in parameterization. We propose a Bayesian information criterion to consistently select the order of the polynomial trend and the number of trigonometric functions, and use a test statistic to determine the number of common factors. The convergence rates for the estimators of the trend and seasonal components and the limiting distribution of the test statistic are established under the setting that the number of time series tends to infinity with the sample size, but at a slower rate. We study the finite‐sample performance of the proposed analysis via simulation, and analyze two real examples. The first example considers modeling weekly PM_2.5 data of 15 monitoring stations in the southern region of Taiwan and the second example consists of monthly value‐weighted returns of 12 industrial portfolios.     

Long-range Dependence: Revisiting Aggregation with Wavelets

The aggregation procedure is a natural way to analyse signals which exhibit long-range-dependent features and has been used as a basis for estimation of the Hurst parameter, H . In this paper it is shown how aggregation can be naturally rephrased within the wavelet transform framework, being directly related to approximations of the signal in the sense of a Haar multiresolution analysis. A natural wavelet-based generalization to traditional aggregation is then proposed: 'a-aggregation'. It is shown that a-aggregation cannot lead to good estimators of H , and so a new kind of aggregation, 'd-aggregation', is defined, which is related to the details rather than the approximations of a multiresolution analysis. An estimator of H based on d-aggregation has excellent statistical and computational properties, whilst preserving the spirit of aggregation. The estimator is applied to telecommunications network data.     

基于量纲分析的气力输送压降模型研究

量纲分析法是建立数学计算模型的一种重要的方法。以实验室气力输送数据为基础,结合量纲分析法,建立了管道压降计算准数关联式,同时将计算结果与实验数据进行对比分析,实验值与计算值总体变化趋势基本相同,偏差较小,并对其中误差相对较大的公式进行了修正,所得准数关联式对化工气力输送中压降的估算具有实际意义。     

Dimensional Analysis of Particle and Liquid Dispersion in Draining Foam

A dimensionless correlation for the axial dispersion of particles and liquid tracer in draining foam has been developed that expresses the dimensionless dispersion as a function of Peclet and Stokes numbers. The correlation is fitted to the data of Lee et al., Coll. and Surf. A. 263 , 320‐329 (2005). The value of the axial dispersion coefficient is independent of the self‐dispersivity. It is seen that Saffman, J. Fluid. Mech. 6 , 321‐349 (1959) model for the dispersion coefficient in solid porous media only provides order of magnitude prediction of the axial dispersion coefficient but it can inform about the dominant mechanisms of dispersion.     

Potential, Temperature and Doping Dependence for Macropore Formation on n-Si with Backside-Illumination

Applying an anodic bias on a silicon HF contact and illuminating the backside of a n-type silicon wafer allows to create macropores. The formation of random macropores is studied in this paper by determination of the influences of the potential, the temperature and the doping level. A statistical approach is used to evaluate the micrographs. The formation of the macroporous layer consists of two phases. Beginning with a plane surface and homogeneous dissolution of silicon, first pores occur after some time. In this nucleation phase the thickness of the homogeneously dissolved Si depends strongly on the doping level and the temperature but only weakly on the applied bias. In a second phase of stable pore growth the density of pores is investigated as a function of temperature and anodic potential. For low doped material we find a strong stabilisation influence of the deep space charge region (SCR) in the nucleation as well as in the stable pore growth phase. Thus an increased anodic bias decreases the density of pores. For highly doped silicon no stabilisation influence of the SCR is found. The pore growth is dominated by the electrochemical dissolution rate, i.e. increasing the potential increases the density of the macropores.     

含时滞测量值下间歇过程的双维状态估计

基于粒子滤波研究了间歇过程的状态估计问题。根据间歇过程双维动态特性,针对关键参数在线检测精度低、离线分析时滞大等问题,分别建立一种双维状态转移模型和时滞测量模型,并利用贝叶斯方法及前/后向平滑,提出一种含时滞测量值下的双维状态估计算法。该算法通过融合先前批次和时滞测量值的信息提高估计精度,并且克服了离线采样周期和时滞时间不确定的问题。在数字仿真和啤酒发酵过程中的仿真应用验证了该算法的有效性。     

有限元分析中橡胶应变能函数的若干形式

介绍在有限元分析中描述橡胶力学性能常用的应变能函数。以应变不变量表示的应变能函数,较常用的有Ｒｉｖｌｉｎ 模型、ｎｅｏＨｏｏｋｅａｎ 模型、ＭｏｏｎｅｙＲｉｖｌｉｎ 模型、ＧｅｎｔＴｈｏｍａｓ 模型、ＮｉｃｈｏｌｓｏｎＮｅｌｓｏｎ 模型等;以伸张率表示的应变能函数常用的有ＶａｌａｎｉｓＬａｎｄｅｌ 模型、ＰｅｎｇＬａｎｄｅｌ 模型和Ｔｏｂｉｓｃｈ 模型、Ｏｇｄｅｎ 模型等。这些模型各有其特点和适用性。     

Fitting Stochastic Volatility Models in the Presence of Irregular Sampling via Particle Methods and the EM Algorithm

Abstract. Stochastic volatility (SV) models have become increasingly popular for explaining the behaviour of financial variables such as stock prices and exchange rates, and their popularity has resulted in several different proposed approaches to estimating the parameters of the model. An important feature of financial data, which is commonly ignored, is the occurrence of irregular sampling because of holidays or unexpected events. We present a method that can handle the estimation problem of SV models when the sampling is somewhat irregular. The basic idea of our approach is to combine the expectation‐maximization (EM) algorithm with particle filters and smoothers in order to estimate parameters of the model. In addition, we expand the scope of application of SV models by adopting a normal mixture, with unknown parameters, for the observational error term rather than assuming a log‐chi‐squared distribution. We address the problems by using state–space models and imputation. Finally, we present simulation studies and real data analyses to establish the viability of the proposed method.     

轮胎与轮辋接触的有限元分析

考虑了轮胎的材料非线性、接触非线性以及大变形等复杂的力学特性，建立轮胎的有限元分析模型利用MARC非线性有限元分析软件．分析了轮胎与轮辋接触的应力／应变场及变形情况。     

三维流化床电极处理电镀废水

介绍了三维流化床电极的原理与特点。总结了三维流化床电极反应器中电流收集板的结构设计重点。着重分析了反应器运行的各个工艺参数,包括电极材料、pH值、槽电流等对处理效果的影响。并列举了三维流化床电极对几种不同性质废水的处理效果。     

Fast Filtering and Smoothing for Multivariate State Space Models

This paper investigates a new approach to diffuse filtering and smoothing for multivariate state space models. The standard approach treats the observations as vectors, while our approach treats each element of the observational vector individually. This strategy leads to computationally efficient methods for multivariate filtering and smoothing. Also, the treatment of the diffuse initial state vector in multivariate models is much simpler than in existing methods. The paper presents details of relevant algorithms for filtering, prediction and smoothing. Proofs are provided. Three examples of multivariate models in statistics and economics are presented for which the new approach is particularly relevant.     

Similarity Solution in Chemical Engineering Guided by Dimensional Analysis

Similarity solution is a classical topic in chemical engineering,frequently encountered in analysis of flow and transport phenomena in semi-infinite domains.In this article,dimensional analysis is applied to resolve systematically the conditions for the existence of similarity solutions and formulate a basic procedure to get such solutions.With several classic examples,the method for finding the suitable combination of independent variables from original ones is demonstrated,so that the original partial differential equation can be transformed into a simpler ordinary differential equation,through which the desired similarity solution is finally achieved.In-depth analysis of one-dimensional diffusion/reaction problems by dimensional analysis results in some new insights.The elaboration is significant for deep insight of similarity solution and its application in chemical engineering.     

载重子午线轮胎接地面有限元分析

借助MSC.MARC有限元分析软件，考虑了轮胎的材料非线性、接触非线性以及大变形等复杂的力学特性，全钢载重子午线轮胎的非线性有限元分析模型用于数值模拟中，分析了轮胎在充气压力及垂直载荷作用下的接地区域接触、轮胎变形情况、接地区的压力分布情况，并给出了轮胎垂直负荷和下沉量的关系。     

PTA装置中冷凝器的有限元分析

利用ANSYS软件对冷凝器进行了有限元分析,内容包括整体结构温度场及热应力的计算,正常工作和开、停车过程中可能出现的7种瞬态和稳态操作工况及危险工况下的应力计算及疲劳寿命评估。计算结果表明该冷凝器的设计安全可靠,整体和各部件均满足强度要求。该分析为此类大型换热器的国产化设计和制造提供了计算依据。     

免充气轮胎与子午线轮胎有限元分析对比*

通过ABAQUS建立特殊结构的免充气轮胎有限元模型,对其静态加载和侧倾工况下进行了模拟,并将计算结果与同规格11.00R20载重子午线轮胎的实测数据作对比分析。研究表明：静载工况下,两者的静刚度曲线基本吻合,下沉量和接地面积接近,免充气胎最大接地压力较小;侧倾工况下,两者的接地面积和静刚度曲线的变化趋势接近。表明该免充气轮胎可以达到同规格子午线轮胎的性能要求。     

新型啮合转子混炼过程有限元模拟分析

对新型啮合转子混炼过程进行有限元模拟分析,并计算啮合转子能完成的预期剪切效果和混合指数概率分布。通过分析啮合转子的流场发现,啮合转子混炼区在两转子啮合区域,对胶料既有捏炼作用,又有剪切作用,可获得温升较低、质量较好的胶料,但剪切效果较小,可能会导致混合效率和生产效率较低。     

Parameter Estimation of Stochastic Processes with Long-range Dependence and Intermittency

This paper considers the case where a stochastic process may display both long-range dependence and second-order intermittency. The existence of such a process is established in Anh, Angulo and Ruiz-Medina (1999). We systematically study the estimation of parameters involved in the spectral density function of a process with long-range dependence and second-order intermittency. An estimation procedure for the parameters is given. Numerical results are presented to support the estimation procedure proposed in this paper.     

Tests for Volatility Shifts in Garch Against Long‐Range Dependence

Many empirical findings show that volatility in financial time series exhibits high persistence. Some researchers argue that such persistency is due to volatility shifts in the market, while others believe that this is a natural fluctuation explained by stationary long‐range dependence models. These two approaches confuse many practitioners, and forecasts for future volatility are dramatically different depending on which models to use. In this article, therefore, we consider a statistical testing procedure to distinguish volatility shifts in generalized AR conditional heteroscedasticity (GARCH) model against long‐range dependence. Our testing procedure is based on the residual‐based cumulative sum test, which is designed to correct the size distortion observed for GARCH models. We examine the validity of our method by providing asymptotic distributions of test statistic. Also, Monte Carlo simulations study shows that our proposed method achieves a good size while providing a reasonable power against long‐range dependence. It is also observed that our test is robust to the misspecified GARCH models.