Estimation Methods of the Long Memory Parameter: Monte Carlo Analysis and Application

Since the seminal paper of Granger & Joyeux (1980), the concept of a long memory has focused the attention of many statisticians and econometricians trying to model and measure the persistence of stationary processes. Many methods for estimating d, the long-range dependence parameter, have been suggested since the work of Hurst (1951). They can be summarized in three classes: the heuristic methods, the semi-parametric methods and the maximum likelihood methods. In this paper, we try by simulation, to verify the two main properties of [dcirc]: the consistency and the asymptotic normality. Hence, it is very important for practitioners to compare the performance of the various classes of estimators. The results indicate that only the semi-parametric and the maximum likelihood methods can give good estimators. They also suggest that the AR component of the ARFIMA (1, d, 0) process has an important impact on the properties of the different estimators and that the Whittle method is the best one, since it has the small mean squared error. We finally carry out an empirical application using the monthly seasonally adjusted US Inflation series, in order to illustrate the usefulness of the different estimation methods in the context of using real data.     

基于MCS方法的高斯仿射利率期限结构模型研究

本文在最小卡方估计方法基础上研究了高斯仿射利率模型的参数识别和估计问题。以标准化高斯模型为起点,从结构化模型和简约化模型参数的函数关系出发研究高斯仿射模型的可识别性,最小卡方估计量继承了结构化模型极大似然估计量的所有渐进性质并保证了参数估计量的可靠性。以上交所2006-2013年隐含于国债价格月度数据的零息票收益率为样本采用最小卡方方法实证研究了高斯仿射期限模型,结论表明高斯仿射模型很好的拟合了观测的期限结构,并且整体上看简约型和结构型参数估计量的统计性质的优劣具有一致性。     

General tests of independence based on empirical processes indexed by functions

The present paper is mainly concerned with the statistical tests of the independence problem between random vectors. We develop an approach based on general empirical processes indexed by a particular class of functions. We prove two abstract approximation theorems that include some existing results as particular cases. Finally, we characterize the limiting behavior of the Möbius transformation of empirical processes indexed by functions under contiguous sequences of alternatives.     

Multiresolution anomaly detection method for fractional Gaussian noise

Driven by network intrusion detection, we propose a MultiResolution Anomaly Detection (MRAD) method, which effectively utilizes the multiscale properties of Internet features and network anomalies. In this paper, several theoretical properties of the MRAD method are explored. A major new result is the mathematical formulation of the notion that a two-scaled MRAD method has larger power than the average power of the detection method based on the given two scales. Test threshold is also developed. Comparisons between MRAD method and other classical outlier detectors in time series are reported as well.     

Semiparametric Mixtures of Symmetric Distributions

We consider in this paper the semiparametric mixture of two unknown distributions equal up to a location parameter. The model is said to be semiparametric in the sense that the mixed distribution is not supposed to belong to a parametric family. To insure the identifiability of the model, it is assumed that the mixed distribution is zero symmetric, the model being then defined by the mixing proportion, two location parameters and the probability density function of the mixed distribution. We propose a new class of M‐estimators of these parameters based on a Fourier approach and prove that they are ‐consistent under mild regularity conditions. Their finite sample properties are illustrated by a Monte Carlo study, and a benchmark real dataset is also studied with our method.     

The First Exit Time Theory Applied to Life Table Data: The Health State Function of a Population and Other Characteristics

In this article, we summarize the main parts of the first exit time theory developed in connection to the life table data and the resulting theoretical and applied issues. New tools arise from the development of this theory and especially the Health State Function and some important characteristics of this function.

We provide both simple and complex models and propose a methodology for reconstructing the health state function from the provided first exit time density distribution (for the appropriate computer program, see http://www.cmsim.net). In the simpler case, this theory is applied for the reconstruction of the so-called Inverse-Gaussian function.