Simple detection of outlying short time series

Sets of relatively short time series arise in many situations. One aspect of their analysis may be the detection of outlying series. We examine the performance of standard normal outlier tests applied to the means, or to simple functions of the means, of AR(1) series, not necessarily of equal lengths. Although unequal lengths of series implies that the means have unequal variances, that are only known approximately, it is shown that nominal significance levels hold good under most circumstances. Thus a standard outlier test can usefully be applied, avoiding the complication of estimating the time series' parameters. The test's power is affected by unequal lengths, being higher when the slippage occurs in one of the longer series     

BAYESIAN SUBSET SELECTION AND MODEL AVERAGING USING A CENTRED AND DISPERSED PRIOR FOR THE ERROR VARIANCE

This article proposes a new data‐based prior distribution for the error variance in a Gaussian linear regression model, when the model is used for Bayesian variable selection and model averaging. For a given subset of variables in the model, this prior has a mode that is an unbiased estimator of the error variance but is suitably dispersed to make it uninformative relative to the marginal likelihood. The advantage of this empirical Bayes prior for the error variance is that it is centred and dispersed sensibly and avoids the arbitrary specification of hyperparameters. The performance of the new prior is compared to that of a prior proposed previously in the literature using several simulated examples and two loss functions. For each example our paper also reports results for the model that orthogonalizes the predictor variables before performing subset selection. A real example is also investigated. The empirical results suggest that for both the simulated and real data, the performance of the estimators based on the prior proposed in our article compares favourably with that of a prior used previously in the literature.     

Divergence-based estimation and testing with misclassified data

The well-known chi-squared goodness-of-fit test for a multinomial distribution is generally biased when the observations are subject to misclassification. In Pardo and Zografos (2000) the problem was considered using a double sampling scheme and ø-divergence test statistics. A new problem appears if the null hypothesis is not simple because it is necessary to give estimators for the unknown parameters. In this paper the minimum ø-divergence estimators are considered and some of their properties are established. The proposed ø-divergence test statistics are obtained by calculating ø-divergences between probability density functions and by replacing parameters by their minimum ø-divergence estimators in the derived expressions. Asymptotic distributions of the new test statistics are also obtained. The testing procedure is illustrated with an example.     

洋桔梗叶盘高频率不定芽诱导的初步研究

采用正交设计法研究了细胞分裂素ZT,生长素NAA,培养基pH值和光照强度对洋桔梗叶盘不定芽诱导的影响.结果表明,实验的4个因子对洋桔梗叶盘不定芽都具有极显著的诱导作用,最佳培养基和培养条件为:MS ZT 0.5 mg/L NAA 0.01 mg/L,培养基pH值为6.3,光照强度为3000 lx.该处理中每叶盘再生不定芽数平均达9.41个.     

大学英语学期测试种种

大学英语测试是大学英语教学中必不可少的一部分 ,合理的测试方法有助于推动教学质量的提高。通过对现行学期测试题型与内容优缺点的阐释 ,提出了改进学期测试的想法 ,并对学期测试中听力、阅读、词汇与结构、作文及口语的题型与内容以及评卷工作的改进进行了具体的探讨。     

学校早期读写训练对儿童读写成绩的影响

利用儿童家庭一学校早期语言与识字能力系列测验对每个目标儿童语言与识字能力发展进行了评定，并解释了这些测量结果与儿童学校早期语言与识字测量指标之间的关系。结果表明：(1)儿童丰富的同伴关系与词汇量、应变识字能力、下定义能力和理解力有关；(2)教师语言的丰富性有助于儿童语言与识字能力发展；(3)识字课程变量与三岁儿童的应变识字成绩和四岁儿童词汇量和故事理解有关；(4)影响儿童语言与识字能力的因素不是单一的，而是一个因素群。     

外语考试的计算机语料库建设

计算机语料库是以语篇为基础对语言进行研究的一门新兴交叉型学科。20世纪90年代以来,应用不断更新的计算机技术建立的各类语料库,使语言研究的手段与方法都发生了巨大变化。但在开发以各类考试材料为语料的专门用途语料库方面,发展却相对滞后。大学英语四、六级考试是我国高等教育最重要的大规模外语考试,对推动大学外语教学起到了功不可没的作用。研究并建成大学英语四、六级考试计算机语料库,对大学外语教学具有很大意义。     

Approximation of density functions by orthogonal series with grouped data

Summary Letg(x) andf(x) be continuous density function on (a, b) and let {ϕ_j} be a complete orthonormal sequence of functions onL ₂(g), which is the set of squared integrable functions weighted byg on (a, b). Suppose that over (a, b). Given a grouped sample of sizen fromf(x), the paper investigates the asymptotic properties of the restricted maximum likelihood estimator of density, obtained by setting all but the firstm of the ϑ_j’s equal to0. Practical suggestions are given for performing estimation via the use of Fourier and Legendre polynomial series. Research partially supported by: CNR grant, n. 93. 00837. CT10.     

Implementing partial least squares

Partial least squares (PLS) regression has been proposed as an alternative regression technique to more traditional approaches such as principal components regression and ridge regression. A number of algorithms have appeared in the literature which have been shown to be equivalent. Someone wishing to implement PLS regression in a programming language or within a statistical package must choose which algorithm to use. We investigate the implementation of univariate PLS algorithms within FORTRAN and the Matlab (1993) and Splus (1992) environments, comparing theoretical measures of execution speed based on flop counts with their observed execution times. We also comment on the ease with which the algorithms may be implemented in the different environments. Finally, we investigate the merits of using the orthogonal invariance of PLS regression to improve the algorithms.