Robustness of the linear discriminant function to nonnormality: Edgeworth series distribution

The effects of applying the normal classificatory rule to a nonnormal population are studied here. These are assessed through the distribution of the misclassification errors in the case of the Edgeworth type distribution. Both theoretical and empirical results are presented. An examination of the latter shows that the effects of this type of nonnormality are marginal. The probability of misclassification of an observation from ∏₁, using the appropriate LR rule, is always larger than one using the normal approximation (μ₁<μ₂). Converse condition holds for the misclassification of an observation from ∏₂. Overall error rates are not affected by the skewness factor to any great extent.     

Detection of outliers in bivariate time series data

In this article, we use the influence function matrix of auto and cross-correlations of a bivariate (multivariate) time series for detecting the outliers. The multivariate analog of the graphical method of Chernick et. al. (1982), to detect outliers and partial outliers is presented. A simulation study illustrating the method is also given.     

Artificial neural networks for some macroeconomic series: A first report

There is a tendency for the true variability of feasible GLS estimators to be understated by asymptotic standard errors. For estimation of SUR models, this tendency becomes more severe in large equation systems when estimation of the error covariance matrix, C, becomes problematic. We explore a number of potential solutions involving the use of improved estimators for the disturbance covariance matrix and bootstrapping. In particular, Ullah and Racine (1992) have recently introduced a new class of estimators for SUR models that use nonparametric kernel density estimation techniques. The proposed estimators have the same structure as the feasible GLS estimator of Zellner (1962) differing only in the choice of estimator for C. Ullah and Racine (1992) prove that their nonparametric density estimator of C can be expressed as Zellner's original estimator plus a positive definite matrix that depends on the smoothing parameter chosen for the density estimation. It is this structure of the estimator that most interests us as it has the potential to be especially useful in large equation systems.

Atkinson and Wilson (1992) investigated the bias in the conventional and bootstrap estimators of coefficient standard errors in SUR models. They demonstrated that under certain conditions the former were superior, but they caution that neither estimator uniformly dominated and hence bootstrapping provides little improvement in the estimation of standard errors for the regression coefficients. Rilstone and Veal1 (1996) argue that an important qualification needs to be made to this somewhat negative conclusion. They demonstrated that bootstrapping can result in improvements in inferences if the procedures are applied to the t-ratios rather than to the standard errors. These issues are explored for the case of large equation systems and when bootstrapping is combined with improved covariance estimation.     

论《彊村丛书》对词籍版本目录的校勘

晚清兴起校刻词籍的热潮,《彊村丛书》是传统词学整理校勘词籍的集大成者。它借鉴刘向广搜异本的校雠家法、乾嘉文献考据方法以及《经义考》的著录体例,考察历代官私目录,考订籍名、梳理版本源流、评价各版本价值、考察存佚、考证词人,完善了词籍版本目录著录的体例和方法,动态展示了晚清词学家群体和藏书家之间的学术交流活动,推动了词籍版本目录学的发展,具有重要的词学文献学意义。     

串联系统可靠性分配的优化方法

研究了串联系统可靠性分配的计算方法，通过实例把以往常用的“序贯”法和以计算机为工具的数学规划法进行比较，说明利用数学规划法，可以使串联系统可靠性分配更加方便、合理。