Alternative approaches to implementing Lagrange multiplier tests for serial correlation in dynamic regression models

An approximate F-form of the Lagrange multiplier (LM) test for serial correlation in dynamic regression models is compared with three bootstrap tests. In one bootstrap procedure, residuals from restricted estimation under the null hypothesis are resampled. The other two bootstrap tests use residuals from unrestricted estimation under an alternative hypothesis. A fixed autocorrelation alternative is assumed in one of the two unrestricted bootstrap tests and the other is based upon a Pitman-type sequence of local alternatives. Monte Carlo experiments are used to estimate rejection probabilities under the null hypothesis and in the presence of serial correlation.     

Mixture Poisson regression models for heterogeneous count data based on latent and fuzzy class analysis

In this paper we propose a new approach, called a fuzzy class model for Poisson regression, in the analysis of heterogeneous count data. On the basis of fuzzy set concept and fuzzy classification maximum likelihood (FCML) procedures we create an FCML algorithm for fuzzy class Poisson regression models. Traditionally, the EM algorithm had been used for latent class regression models. Thus, the accuracy and effectiveness of EM and FCML algorithms for estimating the parameters are compared. The results show that the proposed FCML algorithm presents better accuracy and effectiveness and can be used as another good tool to regression analysis for heterogeneous count data.This work was supported in part by the National Science Council of Taiwan under Grant NSC-89-2213-E-033-007.     

Improved inference on a scalar fixed effect of interest in nonlinear mixed-effects models

Likelihood-based inference on a scalar fixed effect of interest in nonlinear mixed-effects models usually relies on first-order approximations. If the sample size is small, tests and confidence intervals derived from first-order solutions can be inaccurate. An improved test statistic based on a modification of the signed likelihood ratio statistic is presented which was recently suggested by Skovgaard [1996. An explicit large-deviation approximation to one-parameter tests. Bernoulli 2, 145-165]. The finite sample behaviour of this statistic is investigated through a set of simulation studies. The results show that its finite-sample null distribution is better approximated by the standard normal than it is for its first-order counterpart. The R code used to run the simulations is freely available.     

基于支持向量回归机的HHT边界效应处理

针对希尔伯特-黄变换中的边界效应，提出了基于支持向量回归机的时间序列预测方法．在支持向量回归机的应用当中，参数的选取对它的泛化性能有很大影响．在讨论了参数对支持向量回归机的泛化性能的影响基础上，提出了通过微粒群优化算法来优化支持向量回归机参数的方法，使得支持向量回归机在应用中能够自适应的选择最优参数，从而获得了更好的泛化性能，提高了在端点处的延拓精度，很好地抑制了端点效应．试验表明，该优化算法能够很好解决支持向量回归机的参数选取问题．通过与神经网络的延拓方法和黄等人的HHTDPS结果对比，基于支持向量回归机的时间序列预测方法可以更好地解决在希尔伯特-黄变换中存在的边界效应，得到的固有模态函数具有较小的失真．     

传感器网络中时空关联的脏数据过滤技术

研究了无线传感器网络中脏数据过滤问题,提出了基于时空关联的脏数据过滤技术.该技术利用无线传感器网络中感知教据的时空关联特性,建立了时空关联脏数据过滤模型,通过其来过滤无线传感器节点产生的错误数据即脏数据.本地节点通过时间关联进行一次过滤,过滤掉暂时性错误数据;并在一个簇中采用空间关联进行二次过滤,过滤掉永久性错误数据.实验结果表明了该技术的合理性和有效性.