LAMOST光谱J152238.11+333136.1 P-Cygni轮廓分析

LAMOST一期巡天成功获取河外星系光谱超过150 000条,大样本光谱数据为探索奇异、稀有的天体从而完善现有的天体演化理论提供了必要的数据条件;而先进的信息技术为从海量的数据中挖掘这些珍稀样本提供了有效途径。针对采用基于DoPS的数据挖掘方法,从LAMOST DR5星系光谱数据获得的离群数据挖掘结果中,呈现出疑似P-Cygni轮廓特征的光谱J152238.11+333136.1进行了深入讨论。首先针对该光谱的基本信息、疑似P-Cygni轮廓特征以及相应的离群数据挖掘方法进行了简要表述,光谱在Hβ和[OⅢ]λ4860处呈现P-Cygni轮廓,在NeⅢλ3869和HeⅠλ5874处呈现反P-Cygni轮廓;其次,对该特征的真实性及其生成机制从以下4个角度展开讨论。(1)交叉同源观测。Sloan巡天2004年(相差11年)的同源观测,其光谱上并未呈现对应的特征,据推测可能是正在进行的演化活动或者光纤定位误差所致;(2)通过分析光谱质量、减天光残差等方法,分析P-Cygni特征是否为观测或数据处理所致。NeⅢλ3869和HeⅠλ5874处呈现反P-Cygni轮廓可信度较低;同时,通过比较目标光谱与超级天光,以及相邻光纤观测到的光谱在对应波长处的光谱特征,说明存在P-Cygni轮廓为减天光过程导致的可能性;(3)光谱子型差异。IRAS和WISE等近红外同源观测,显示其为Seyfert 2型星系,光学波段发射线强比[NⅡ]/Hα,[OⅢ]/Hβ显示其为HⅡ区,结合光学、红外测光图像特征,推测目标可能是两个星系进行并合活动;(4)从导致P-Cygni轮廓的物理机制的角度,分析了由星系并合触发外流、由恒星形成(爆发)电离气体触发的外流以及由Wolf-Rayet特征星系的超星风等原因引起的可能性。     

基于IFSR异常样本剔除的落叶松木材密度近红外优化模型的研究

木材密度可以反映木材的干缩性、抗压抗拉强度等多种物理性质,是重要的木材物理特性。采用近红外光谱技术能够实现木材密度的快速预测,可克服传统检测方法耗费人力、物力、时间的弊端,但建模结果往往受异常样本的影响。为准确识别并剔除样本集中的异常样本,提出一种孤立森林结合学生化残差方法(IFSR),在利用孤立森林集成特征的优点基础上考虑样本对模型的影响度,可同时检测异常样本与强影响样本。该研究对181个落叶松木材样本的近红外光谱及其在常温下的气干密度进行了测定。通过对比多种方法预处理和特征选择方法,确定采用标准正态变量变化(SNV)+去趋势处理(DT)+均值中心化(MC)+标准化(Auto)方法进行预处理,采用竞争性自适应重加权算法(CARS)进行特征波段选择,消除噪声及无关信息对算法的影响,简化数据集,提高算法剔除异常样本的准确性。为验证IFSR方法剔除异常样本的能力,将其与蒙特卡洛交互验证(MCCV)、马氏距离(MD)等其他六种异常检测方法对比分析,建立偏最小二乘(PLS)模型对其进行异常检测性能评价。同时在上述基础上采用粒子群寻优-支持向量机回归(PSO-SVR), BP神经网络(BPNN)...     

Outlier Mining Based on Principal Component Estimation

Outlier mining is an important aspect in data mining and the outlier mining based on Cook distance is most commonly used. But we know that when the data have multicoUinearity, the traditional Cook method is no longer effective. Considering the excellence of the principal component estimation, we use it to substitute the least squares estimation, and then give the Cook distance measurement based on principal component estimation, which can be used in outlier mining. At the same time, we have done some research on related theories and application problems.     

基于顺序假设的异常值识别方法

针对样本标准偏差未知的情况,提出了基于顺序假设的异常值识别方法.仿真实验表明该方法能同时对多个异常值进行识别,与拉依达准则相比较,该方法对异常值具有更高的识别率.     

Influence diagnostics and outlier tests for varying coefficient mixed models

In this paper, we consider subset deletion diagnostics for fixed effects (coefficient functions), random effects and one variance component in varying coefficient mixed models (VCMMs). Some simple updated formulas are obtained, and based on which, Cook’s distance, joint influence and conditional influence are also investigated. Besides, since mean shift outlier models (MSOMs) are also efficient to detect outliers, we establish an equivalence between deletion models and MSOMs, which is not only suitable for fixed effects but also for random effects, and test statistics for outliers are then constructed. As a byproduct, we obtain the nonparametric “delete = replace” identity. Our influence diagnostics methods are illustrated through a simulated example and a real data set.     

Robust regression techniques A useful alternative for the detection of outlier data in chemical analysis

The validation of an analytical procedure means the evaluation of some performance criteria such as accuracy, sensitivity, linear range, capability of detection, selectivity, calibration curve, etc. This implies the use of different statistical methodologies, some of them related with statistical regression techniques, which may be robust or not. The presence of outlier data has a significant effect on the determination of sensitivity, linear range or capability of detection amongst others, when these figures of merit are evaluated with non-robust methodologies.In this paper some of the robust methods used for calibration in analytical chemistry are reviewed: the Huber M-estimator; the Andrews, Tukey and Welsh GM-estimators; the fuzzy estimators; the constrained M-estimators, CM; the least trimmed squares, LTS. The paper also shows that the mathematical properties of the least median squares (LMS) regression can be of great interest in the detection of outlier data in chemical analysis. A comparative analysis is made of the results obtained by applying these regression methods to synthetic and real data. There is also a review of some applications where this robust regression works in a suitable and simple way that proves very useful to secure an objective detection of outliers. The use of a robust regression is recommended in ISO 5725-5.     

一种基于光谱奇异值检测的高光谱遥感小目标探测方法

高光谱遥感技术能够借助丰富的地物图像和光谱信息,反映目标地物与背景地物间的细微差异,从而将其区分开来。目前的小目标探测算法多侧重于从图像处理方面着手,文章则从光谱维数据分析的角度出发,利用光谱分析中的奇异值检测方法探测小目标,首先对关注区域的地物像元光谱进行连续统去除和正交变换等预处理;然后将每个像元的光谱对该区域平均光谱进行光谱匹配求其相似性,并实现高光谱数据降维;而后通过光谱角匹配值的马氏距离进行奇异值检测,将马氏距离大于自适应阈值的像元判定为小目标。该方法不需要任何先验信息,实验结果表明该方法运算量较小,运算速度快,并有较好的小目标探测准确度。     

The statistical basis of standardization designs for diagnostic assays

Accuracy and long-term quality of laboratory diagnostic assays depend critically on the standardization process. In this note we review statistically typical procedures and designs used in standardization. Issues of practical relevance such as the number of systems, runs, and replicates to be involved in the standardization design, and quality control aspects, are addressed.     

双参数对数正态分布异常数据的检测方法

针对双参数对数正态分布场合下的多个异常数据给出一种新的检测方法。首先由参数的BLUE导出证明了两个检验所用的枢轴量,然后通过蒙特卡罗方法模拟得到枢轴量的样本分位点表,最后用一个例子说明方法是有效可行的。