Recurrence relations among moments of order statistics from two related sets of independent and non-identically distributed random variables

Some recurrence relations among moments of order statistics from two related sets of variables are quite well-known in the i.i.d. case and are due to Govindarajulu (1963a, Technometrics, 5, 514–518 and 1966, J. Amer. Statist. Assoc., 61, 248–258). In this paper, we generalize these results to the case when the order statistics arise from two related sets of independent and non-identically distributed random variables. These relations can be employed to simplify the evaluation of the moments of order statistics in an outlier model for symmetrically distributed random variables.     

Inapplicability of Asymptotic Results on the Minimal Spanning Tree in Statistical Testing

Penrose has given asymptotic results for the distribution of the longest edge of the minimal spanning tree and nearest neighbour graph for sets of multivariate uniformly or normally distributed points. We investigate the applicability of these results to samples of up to 100 points, in up to 10 dimensions. We conclude that the asymptotic results provide an acceptable approximation only in the uniform case. Their inaccuracy for the multivariate normal case means that they cannot be applied to improve Rohlf's gap test for an outlier in a set of multivariate data points, which depends on the longest edge of the minimal spanning tree of the set.     

Recurrence relations for order statistics from n independent and non-identically distributed random variables

Some well-known reeurrence relations for order statistics in the i.i.d. case are generalized to the case when the variables are independent and non-identically distributed. These results could be employed in order to reduce the amount of direct computations involved in evaluating the moments of order statistics from an outlier model.     

Relationships between moments of two related sets of order statistics and some extensions

Govindarajulu expressed the moments of order statistics from a symmetric distribution in terms of those from its folded form. He derived these relations analytically by dividing the range of integration suitably into parts. In this paper, we establish these relations through probabilistic arguments which readily extend to the independent and non-identically distributed case. Results for random variables having arbitrary multivariate distributions are also derived.The first author would like to thank the Natural Sciences and Engineering Research Council of Canada for funding this research.     

Robust M-estimation of a dispersion matrix with a structure

An iterative algorithm for the robust M-estimation of the dispersion matrix of the form + ² I _p has been given. This algorithm converges after some steps and reduces the effect of outliers on the covariance matrix. The consistency and asymptotic normality of the estimator are established.     

A Review of Robust Regression and Diagnostic Procedures in Linear Regression

The concern over outliers is old since Bernoulli (see [12]), reviewed historically by [11] and updated with [10] in their encyclopedia textbook. James et al.~([46]) used simulation technique to compare some recent published outlier detection procedures.The history of adept and diagnosis of outliers is traced from old and presence comments. Theil-type or Rank, Brown-Mood, L_p, M, adaptive M, GM, and Trimmed-Winsorization estimators are the most popular estimators that we will review in this paper as an application to outlier accommodation. We will review and compare the most numerical and graphical displays based on residuals to flag outliers.     

近红外光谱分析中异常值的判别与定量模型优化

介绍了利用马氏距离、Cook距离、光谱特征异常值、光谱残差比、化学值绝对误差等指标结合数理统计检验来判断光谱和化学值的异常 ,并利用这些方法进行近红外光谱定量分析中模型优化 ,取得了很好的效果     

基于Gibbs抽样的贝叶斯稳健ARMA模型研究

针对ARMA模型建模过程中模型识别和参数估计易受观测值异常点影响问题,构建了同时考虑加性异常点和更新性异常点的ARMA模型．运用基于Gibbs抽样的Markov Chain Monte Carlo贝叶斯方法,估计稳健ARMA模型参数,同步确定观测值中异常点的位置,辨别异常点类型．并利用我国人口自然增长数据进行仿真分析,研究结果表明：贝叶斯方法能够有效地识别ARMA序列的异常点．     

Quadratic mixed integer programming and support vectors for deleting outliers in robust regression

We consider the problem of deleting bad influential observations (outliers) in linear regression models. The problem is formulated as a Quadratic Mixed Integer Programming (QMIP) problem, where penalty costs for discarding outliers are used into the objective function. The optimum solution defines a robust regression estimator called penalized trimmed squares (PTS). Due to the high computational complexity of the resulting QMIP problem, the proposed robust procedure is computationally suitable for small sample data. The computational performance and the effectiveness of the new procedure are improved significantly by using the idea of ε-Insensitive loss function from support vectors machine regression. Small errors are ignored, and the mathematical formula gains the sparseness property. The good performance of the ε-Insensitive PTS (IPTS) estimator allows identification of multiple outliers avoiding masking or swamping effects. The computational effectiveness and successful outlier detection of the proposed method is demonstrated via simulated experiments. This research has been partially funded by the Greek Ministry of Education under the program Pythagoras II.     

稳健稀疏主成分分析法及其实证研究

主成分分析是多元统计分析中一种非常经典的降维技术。然而,经典主成分分析却是对离群值非常敏感的,常因离群值的存在导致结果与实际不相符。另一方面,当主成分分析用于综合评价时,主成分的含义常因载荷间绝对值大小不分明而含糊不清,从而导致综合评价难以展开。本文通过使用稳健稀疏主成分分析法进行模拟实验和实证分析,结果表明:该方法不仅能很好地抵抗离群值的影响,而且还能准确地识别出离群样本。通过该方法得出的主成分的含义也较经典主成分分析和稳健主成分分析更加地明确和贴近实际。