Generalized RCINAR(1) Process with Signed Thinning Operator

A generalized random coefficient first-order integer-valued autoregressive process with signed thinning operator is introduced, this kind of process is appropriate for modeling negative integer-valued time series. Strict stationarity and ergodicity of process are established. Estimators of the parameters of interest are derived and their properties are studied via simulation. At last, we use bootstrap method in the real data analysis.     

An Empirical Strategy to Detect Spurious Effects in Long Memory and Occasional-Break Processes

Long-range dependence and structural changes in level are intimely related phenomena and it is very difficult to separate the two effects. In this article, we present an empirical procedure to distinguish between long-memory and occasional-break processes. An extensive Monte Carlo experiment illustrates the performance of the procedure and an application to real data is also included.     

Information matrix for a mixture of two inverse gaussian distributions

In this paper, Fisher information matrix about the five parameters ρ, μ_:1, μ², λ¹and λ²of a mixture of two Inverse Gaussian density functions is obtained. The Leguerre-Gauss quadrature formula is used to evaluate the essential integral on which the twenty five elements of the information matrix are based. Results involving the computation of the information about p are compared with those involving both the power series expansion and Simpson's method of integration. Laguerre-Gauss quadra-ture was found to lead to good approximations as compared with other methods. It was therefore chosen for the computations of the elements of the information matrix.     

Multiple Time Series Modeling and Extended Sample Cross-Correlations

This article provides an expository account of the multivariate autoregressive moving average models and proposes an extended sample cross-correlation approach for practical model identification. An iterative model building procedure for applying these models to real data is discussed and demonstrated by analyzing the 5-series U.S. Hog Data.     

中国民间美术元素在油画创作中的运用

中国民间美术元素在油画创作中能发挥重要作用。从形式上,借用代表我国民族文化标识的瓷器的碎片、皮影等作为油画创作的语言元素,加以重新组合构成,赋予新的时代意义。从精神内涵上,从油画创作中去找寻文化之源,去追寻文化的历史,通过对历史寻源,找寻到艺术创作的立足点,用今天的、现代的、原生态的观点和思想对艺术传统和历史进行新的诠释与解读。     

Nonparametric regression estimation in a null recurrent time series

We derive an asymptotic theory of nonparametric estimation for a time series regression model Z_t=f(X_t)+W_t, where {X_t} and {Z_t} are observed nonstationary processes, and {W_t} is an unobserved stationary process. The class of nonstationary processes allowed for {X_t} is a subclass of the class of null recurrent Markov chains. This subclass contains the random walk, unit root processes and nonlinear processes. The process {W_t} is assumed to be linear and stationary.     

Asymptotic robustness of least median of squares for autoregressions with additive outliers

This paper considers the robustness properties in the time series context of the least median of squares (LMS) estimator. The influence function of the LMS estimator is derived under additive outlier contamination. This influence function is redescending and bounded for fixed values of the AR parameters. The gross-error sensitivity, however, is an unbounded function of the AR parameters. In order to asses the global robustness behavior of the LMS estimator, we consider several notions of breakdown. The breakdown points of the LMS estimator depend on the value of the underlying AR parameter. Generally, the breakdown point is below one half for high values of the AR parameter. The bias curves of the LMS estimator reveal, however, that the magnitude of outliers has to be considerable in order to cause breakdown.     

堆垒级数部分和的一般公式

利用阶乘幂差分的重要性质,得到堆垒级数部分和的一般公式.     

News

U. S. National Income Series Revised—Congress Votes No on Censuses of Business and Manufactures—Britain Revises Living Cost Index-U. S. and U. K. Surveys Uncover Lacks in Statistical Training-Forthcoming Statistical Conferences     

Robust estimation of AR coefficients under simultaneously influencing outliers and missing values

A family of robust estimators for coefficients of Gaussian AR(p) time series under simultaneously influencing distortions of two types: outliers and missing values, is proposed. The estimators are based on special properties of the Cauchy probability distribution; consistency and the asymptotic normality of these estimators are proven. An approximate solution of the problem of minimization of the asymptotic variance within the proposed family of estimators is found. Performance of the proposed estimators is illustrated for simulated time series and for real data sets.