Finite sample properties of ml and reml estimators in time series regression models with long memory noise

Finite sample properties of ML and REML estimators in time series regression models with fractional ARIMA noise are examined. In particular, theoretical approximations for bias of ML and REML estimators of the noise parameters are developed and their accuracy is assessed through simulations. The impact of noise parameter estimation on performance of t -statistics and likelihood ratio statistics for testing regression parameters is also investigated.     

新的图像加密方法

针对数字图像信息数据量大、冗余度高和像素间相关性强等特点,提出了一种基于二元多项式的图像加密新方法。在对图像的加密过程中,该方法使用另一幅图像作为密钥,使得密钥形象直观且伪装性强,而密钥图像的尺寸可以远远小于加密图像,便于保存。因加密的大部分步骤中只用到了有限域的加法运算,因此该算法的加密效率较高。该加密方法不仅有安全性高和便于图像的局部加密等优点,还可以方便地推广到视频的图像加密领域,具有很好的应用前景。     

怎样的制造企业更愿涉足服务业务?———“服务化困境”问题的生产率匹配探析

本文关注于制造业企业服务化过程中的产业效率因素,设计了差异化产品模型。在需求层面,公司所提供的产品和服务之间具有互补性;而在供应方面,企业在产品生产和服务之间在专业知识分配上存在竞争。理论模型提供了对制造企业服务化与全要素生产率之间的计量评估。本文利用我国上市制造业版块中的企业数据,通过使用具有异质性的Fractional Probit 模型控制内生性并进行实证检验,理论模型推导和经验估计指出,企业全要素生产率与服务化之间存在“U”型关系,针对四个行业部门的检验也证实服务化和企业全要素生产率之间的非线性关系。在不同地区的企业之间,“U”型关系也存在差异。企业所处行业环境、所在区域以及所提供产品和服务的内在特征对于“服务化困境”的表现都存在差异性的影响。     

A SCORE TEST FOR SEASONAL FRACTIONAL INTEGRATION AND COINTEGRATION

This paper develops a time domain score statistic for testing fractional integration at zero and seasonal frequencies in quarterly time series models. Further, it introduces the notion of fractional cointegration at different frequencies between two seasonally integrated, I(1) series. In testing problems involving seasonal fractional cointegration, it is argued that the alternative hypothesis is one-sided for which the usual score test may not be appropriate. Therefore, based on ideas in Silvapulle and Silvapulle (1995), a one-sided score statistic is constructed. A simulation study finds that the score statistic generally has desirable size and power properties in moderately sized samples. The score test is applied to the quarterly Australian consumption function. The income and consumption series are found to be I(1) at zero and seasonal frequencies and these two series are not cointegrated at any frequency.     

Defining equations for two-level factorial designs

Defining equations are introduced in the context of two-level factorial designs and they are shown to provide a concise specification of both regular and nonregular designs. The equations are used to find orthogonal arrays of high strength and some optimal designs. The latter optimal designs are formed in a new way by augmenting notional orthogonal arrays which are allowed to have some runs with a negative number of replicates before augmentation. Defining equations are also shown to be useful when the factorial design is blocked.     

The Equivalence of Neyman Optimum Allocation for Sampling and Equal Proportions for Apportioning the U.S. House of Representatives

We present a surprising though obvious result that seems to have been unnoticed until now. In particular, we demonstrate the equivalence of two well-known problems—the optimal allocation of the fixed overall sample size n among L strata under stratified random sampling and the optimal allocation of the H = 435 seats among the 50 states for apportionment of the U.S. House of Representatives following each decennial census. In spite of the strong similarity manifest in the statements of the two problems, they have not been linked and they have well-known but different solutions; one solution is not explicitly exact (Neyman allocation), and the other (equal proportions) is exact. We give explicit exact solutions for both and note that the solutions are equivalent. In fact, we conclude by showing that both problems are special cases of a general problem. The result is significant for stratified random sampling in that it explicitly shows how to minimize sampling error when estimating a total T_Y while keeping the final overall sample size fixed at n; this is usually not the case in practice with Neyman allocation where the resulting final overall sample size might be near n + L after rounding. An example reveals that controlled rounding with Neyman allocation does not always lead to the optimum allocation, that is, an allocation that minimizes variance.