异常观测数据的逐点剔除法

本文通过引进一类特殊的统计量和对其统计性质的分析,得到了一种线性回归函数因变量异常观测数据的逐点剔除方法,该方法能很快地认识和剔除含粗大的误差的观测数据,具有低的误判概率。本文还给出了与本文方法配套的数值算法,说明本文方法的数值实例和仿真结果。     

离群数据挖掘方法研究

离群数据挖掘是数据挖掘技术的重要研究领域之一。在简单介绍了离群数据挖掘的产生背景及研究意义后,对几种主要的离群数据挖掘方法进行了简单的阐述,详细论述了基于距离的离群数据挖掘算法和基于密度的离群数据挖掘算法,希望能为离群数据挖掘的研究者提供借鉴。     

无源定位跟踪中野值的检测与剔除方法

针对无源定位跟踪中野值的出现会降低滤波的可靠性和稳定性问题,结合新息似然的概念提出了一种基于似然的野值检测与剔除方法。通过计算卡尔曼滤波更新中得到的似然值,设定门限,以达到野值的检测与剔除的目的。仿真结果表明,该算法有效地处理了野值对定位跟踪精度的影响,使得目标定位跟踪精度有了较大的提高。     

对聚类算法普遍存在问题的解决办法

聚类广泛应用于统计、机器学习、模式识别、数据分析等领域并越来越受重视。本文研究了各种聚类算法共同面临的五个问题：聚类效果评估、类数目估计、数据预处理、样本间相似性测量、抗干扰性能，分析了对这些问题的有代表性的解决方法，总结并预测了未来聚类算法在这五个方面的研究方向。     

基于灰色关联度-云模型的群评价数据质量改进方法及应用研究

针对群评价中的数据质量问题, 从评价专家和评价值两个角度进行了异常数据处理, 提出了基于灰色关联度-云模型的群评价数据质量改进方法。基于改进的云距离模型测算被评价对象云和目标云之间的差距, 采用TOPSIS法进行评价排序。将数据质量改进方法和云距离模型用于区域物流竞争力群评价, 改进了群评价的数据质量, 提高了评价结果的稳定性和代表性。     

Multivariate calibration maintenance and transfer through robust fused LASSO

This article studies calibration maintenance and transfer to build a statistical model that is able to predict analyte concentrations by a set of spectra. Noticing that the wavelength atoms are naturally ordered in a meaningful way, we propose a novel robust fused LASSO (RFL) based on high‐dimensional sparsity techniques and a recent Θ‐IPOD technique for robustification. This new approach can attain simultaneous wavelength selection and grouping as well as outlier identification, without any human intervention. An efficient and scalable algorithm is developed on the basis of the alternating direction method of multipliers. The obtained RFL model is sparse and shows improved prediction performance over the LASSO and ridge regression. Our results reveal that wavelengths can be combined into blocks, in a smart manner, to enhance the interpretability and reliability for super‐resolution spectral analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

Wavelet-Based Detection of Outliers in Time Series

This article considers the problem of detecting outliers in time series data and proposes a general detection method based on wavelets. Unlike other detection procedures found in the literature, our method does not require that a model be specified for the data. Also, use of our method is not restricted to data generated from ARIMA processes. The effectiveness of the proposed method is compared with existing outlier detection procedures. Comparisons based on various models, sample sizes, and parameter values illustrate the effectiveness of the proposed method.     

Outlier Detections in Autoregressive Models

This article proposes a new technique for detecting outliers in autoregressive models and identifying the type as either innovation or additive. This technique can be used without knowledge of the true model order, outlier location, or outlier type. Specifically, we perturb an observation to obtain the perturbation size that minimizes the resulting residual sum of squares (SSE). The reduction in the SSE yields outlier detection and identification measures. In addition, the perturbation size can be used to gauge the magnitude of the outlier. Monte Carlo studies and empirical examples are presented to illustrate the performance of the proposed method as well as the impact of outliers on model selection and parameter estimation. We also obtain robust estimators and model selection criteria, which are shown in simulation studies to perform well when large outliers occur.     

单站无源定位中的抗野值鲁棒CKF算法

单站无源定位系统的测量噪声中如果出现野值,会影响滤波器的估计精度和稳定性,严重时还会导致滤波器发散。针对这一问题,基于Bayes定理并结合归一化受污染正态模型,提出了一种抗野值鲁棒容积卡尔曼滤波算法。该算法采用球面径向积分原则直接计算非线性函数的均值和方差,并对测量误差建立一个归一化的受污染正态模型,然后根据野值出现的后验概率来自适应调整测量预测残差的方差阵。结合空频域单站无源定位模型进行仿真实验表明,该算法可以较好地抑制测量噪声中的离散或成片连续野值的不利影响,具有较强的鲁棒性。