EMPIRICAL BAYES TEST PROBLEMS OF VARIANCE COMPONENTS IN RANDOM EFFECTS MODEL

Bayes decision rule of variance components for one-way random effects model is derived and empirical Bayes (EB) decision rules are constructed by kernel estimation method. Under suitable conditions, it is shown that the proposed EB decision rules are asymptotically optimal with convergence rates near O(n-1/2). Finally, an example concerning the main result is given.

ROBUST ESTIMATION OF VARIANCE COMPONENTS MODEL

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Estimation and accuracy evaluation of variance and covariance components based on more general functional model

Started from the more general functional model and based on the work of Koch K R (1986) and QU Zi qiang (1989), marginal likelihood function of variance and covariance components is derived and is identical with the orthogonal complement likelihood function. Minimum norm quadratic unibiased estimator (MINQUE) is developed, which expands the formula by Rao C R (1973). It is proved that Helmert type estimation, MINQUE, BQUE(Best quadratic unibiased estimation) and maximum likelihood estimation are identical with one another. Besides, a universal formula for accuracy evalution is presented. Through these work, a universal theory of variance and covariance components is established.

Nonlinear total least-squares variance component estimation for GM(1,1)model

The solution of the grey model(GM(1,1)model)generally involves equal-precision observations,and the(co)variance matrix is established from the prior information.However,the data are generally available with unequal-precision measurements in reality.To deal with the errors of all observations for GM(1,1)model with errors-in-variables(EIV)structure,we exploit the total least-squares(TLS)algorithm to estimate the parameters of GM(1,1)model in this paper.Ignoring that the effect of the improper prior stochastic model and the homologous observations may degrade the accuracy of parameter estimation,we further present a nonlinear total least-squares variance component estimation approach for GM(1,1)model,which resorts to the minimum norm quadratic unbiased estimation(MINQUE).The practical and simulative experiments indicate that the presented approach has significant merits in improving the predictive accuracy in comparison with control methods.

UNIVERSAL FORMULAE OF ROBUST ESTIMATES OF PARAMETER VECTOR AND VARIANCE COMPONENTS

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MAXIMUM ORTHOGONAL COMPLEMENT LIKELIHOOD ESTIMATION FOR VARIANCE COMPONENTS

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Comprehensive Study on the Estimation of the Variance Components of Traverse Nets

This paper advances a new simplified formula for estimating variance components ,sums up the basic law to calculate the weights of observed values and a circulation method using the increaments of weights when estimating the variance components of traverse nets,advances the charicteristic roots method to estimate the variance components of traveres nets and presents a practical method to make two real and symmetric matrices two diagonal ones.

Impact of sampling interval on variance components of epoch-wise residual error in relative GPS positioning: A case study of a 40-km-long baseline

The study presents sampling interval impacts on variance components of the epoch-wise residual errors in relative GPS positioning. In the variance components estimation process, the 2-way nested ANOVA method was used. For that purpose, GPS observation data during four months at two permanent GPS stations, establishing a 40-km-long baseline as a part of the Montenegrin permanent network(Monte Pos), were used. The study results showed that there is no statistically significant impact of sampling interval changes on epoch-wise variance components related to the residual tropospheric and ionospheric delays(effect a) when it comes to such a baseline. However, it is not the case with epoch-wise variance components related to the interstation-distance-independent residual ‘far-field’ multipath effect(effect b). It turned out that the absolute values of relative differences of standard deviations of the effect a on the relative GPS coordinates(e, n and u) had maximum values 11.1%, 10.2% and 8.9%,respectively. Keeping the same order of presentation for the effect b, the values of 5.9%, 9.9% and 12.5%were obtained. In addition, absolute values of relative differences of standard deviations of horizontal and vertical position had maximum values of 3.8% and 7.7%, respectively.

Evaluation of Third-Order Method for the Tests of Variance Component in Linear Mixed Models

Mixed models provide a wide range of applications including hierarchical modeling and longitudinal studies. The tests of variance component in mixed models have long been a methodological challenge because of its boundary conditions. It is well documented in literature that the traditional first-order methods: likelihood ratio statistic, Wald statistic and score statistic, provide an excessively conservative approximation to the null distribution. However, the magnitude of the conservativeness has not been thoroughly explored. In this paper, we propose a likelihood-based third-order method to the mixed models for testing the null hypothesis of zero and non-zero variance component. The proposed method dramatically improved the accuracy of the tests. Extensive simulations were carried out to demonstrate the accuracy of the proposed method in comparison with the standard first-order methods. The results show the conservativeness of the first order methods and the accuracy of the proposed method in approximating the p-values and confidence intervals even when the sample size is small.

Robust Variance Components Estimation in the PERG Mixed Distributions of Empirical Variances—PEROBVC Method

A mixed distribution of empirical variances, composed of two distributions the basic and contaminating ones, and referred to as PERG mixed distribution of empirical variances, is considered. In the paper a robust inverse problem solution is given, namely a (new) robust method for estimation of variances of both distributions—PEROBVC Method, as well as the estimates for the numbers of observations for both distributions and, in this way also the estimate of contamination degree.

Maximum Posterior Adjustment of Extended Network and Estimation Formulae of Its Variance Components

This paper derives the maximum posterior adjustment formulae of the extended network and the estimation formulaes of variance components of Helmert, Welsch and Frstner types when there are two types of uncorrelated observations in it, and perfects the theory of the maximum posterior adjustment.

Comparison of REML and MINQUE for Estimated Variance Components and Predicted Random Effects

Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis is placed on comparing the properties of two LMM approaches: restricted maximum likelihood (REML) and minimum norm quadratic unbiased estimation (MINQUE) with and without resampling techniques being included. Bias, testing power, Type I error, and computing time were compared between REML and MINQUE approaches with and without Jackknife technique based on 500 simulated data sets. Results showed that MINQUE and REML methods performed equally regarding bias, Type I error, and power. Jackknife-based MINQUE and REML greatly improved power compared to non-Jackknife based linear mixed model approaches. Results also showed that MINQUE is more time-saving compared to REML, especially with the use of resampling techniques and large data set analysis. Results from the actual cotton data analysis were in agreement with our simulated results. Therefore, Jackknife-based MINQUE approaches could be recommended to achieve desirable power with reduced time for a large data analysis and model simulations.

Analysis of stable components in the extended-range forecast for the coming 10-30 days in winter 2010 and 2011

In this paper we try to extract stable components in the extended-range forecast for the coming 10–30 days by using empirical orthogonal function （EOF） analysis, similarity coefficient, and some other methods based on the National Center for Environmental Prediction （NCEP）/National Center for Atmospheric Research （NCAR） reanalysis daily data. The comparisons of the coefficient of variance of climatological background field and truth data in winter between 2010 and 2011 are made. The method of extracting stable components and climatological background field can be helpful to increase forecasting skill. The forecasting skill improvement of air temperature is better than geopotential height at 500 hPa. Moreover, this method improves the predictability better in the Pacific Ocean. In China, the forecast in winter in Northeast China is more uncertain than in the other parts.

Dependence of epoch-wise two-way nested ANOVA estimates of variances of unmodeled effects present in relative GPS positioning on satellite elevation cutoff angle and PDOP mask

Impact of satellite elevation cutoff angle and position dilution of precision(PDOP)mask change on epoch-wise variance components of unmodeled effects that accompany relative Global Positioning System(GPS)positioning is presented herein.Data used for this study refer to the winter and summer periods of the years with minimal(2008)and maximal(2013)solar activity.These data were collected every 30 s in static mode,at two permanent GPS stations located in Montenegro,establishing a mediumdistance(116-km-long)baseline with a height difference of approximately 760 m between its endpoints.The study showed that changing satellite elevation cutoff angle,with a fixed PDOP mask,affects epochwise two-way nested ANOVA estimates of variances related to the‘far-field’multipath(considered as the nested factor herein)and the combined unmodeled effect of tropospheric and ionospheric refraction(considered as the nesting factor herein).However,changing of PDOP mask,with a fixed satellite elevation cutoff angle,doesn’t affect epoch-wise two-way nested ANOVA estimate of variance of the combined unmodeled effect of tropospheric and ionospheric refraction,but,generally,affects the estimate of variance of the‘far-field’multipath(possibly mixed with a part of a‘shorter-term’ionospheric refraction),which is especially pronounced for the summer period.It should also be noted that there is a significant influence of satellite elevation cutoff angle change on both epoch-wise horizontal and vertical position accuracy,only for the summer period,especially in the presence of maximal solar activity,while there is no significant impact of PDOP mask change on epoch-wise positional accuracy.

Genotypic variance in 13C-photosynthate partitioning and within-plant boll distribution in cotton

Background:Photosynthate partitioning and within-plant boll distribution play an important role in yield formation of cotton;however,if and how they interact to mediate yield remains unclear.The objective of this study was to investigate the genotypic variance in photosynthate partitioning and within-plant boll distribution,with a focus on their interactions with regard to yield and yield components.A field experiment was conducted in the Yellow River region in China in 2017 and 2018 using a randomized complete block design with three replicates.Photosynthate partitioning of three commercial cultivars(DP 99 B,Lumianyan 21 and Jimian 169),varying in yield potential,to different organs(including bolls)at early flowering,peak flowering,and peak boll-setting stages,as well as withinplant boll distribution at harvest,and their effects on yield formation were examined.Results:Lint yield of Jimian 169 was the highest,followed by Lumianyan 21 and DP 99 B.Similar differences were observed in the number of inner bolls and boll weight among the three cultivars.J169 partitioned significantly more photosynthate to the fruit and fiber than Lumianyan 21 and DP 99 B and allocated over 80%of assimilates to the inner bolls.Additionally,Lumianyan 21 allocated a higher proportion of photosynthate to bolls and fiber,with12.5%–17.6%more assimilates observed in the inner bolls,than DP 99 B.Conclusions:Genotypic variance in lint yield can be attributed to differences in the number of inner bolls and boll weight,which are affected by photosynthate partitioning.Therefore,the partitioning of photosynthate to fiber and inner bolls can be used as an important reference for cotton breeding and cultivation.

基于机器学习的茶树DNA聚类算法

为了研究茶树基因序列的聚类问题,设计一种基于累计方差贡献率进行改进的核主成分分析(KPCA)与k均值(k-means)++聚类算法相结合的降维聚类算法(KPCA-k-means++)。将基因库数据集筛选分组后,利用k-mers算法提取基因数据的数据特征,根据累计方差贡献率的占比大于85%的标准确定降维主元个数对KPCA进行降维改进并采用k-means++算法对降维后数据聚类,通过CH(Calinski-Harabaze Index)指标和响应时间分析聚类结果。结果表明:在单独聚类、KPCA聚类、改进PCA聚类、改进KPCA聚类4种处理方式中,改进KPCA-k-means++算法在不同处理方式和不同样本数的对比下,CH指标均为最高,与未改进时相比平均高出33%。在响应时间方面,改进KPCA-k-means++算法与同样改进PCA-k-means++算法在不同聚类数和样本数的对比下响应时间均较短。改进KPCA-k-means++算法能够保证对于茶树的基因序列的聚类准确率和聚类速度,表现出极好的聚类稳定性。

通信干扰下无线传感器网络中微弱信号检测

微弱信号检测是保证无线传感器网络高效使用的重要环节,但检测过程易受噪声信号、传感器性能、虚拟信号等因素的干扰,从而导致误检。为了解决上述问题,提出一种通信干扰下无线传感器网络微弱信号检测方法。通过局部投影降噪法剔除信号中的噪声,避免噪声对检测过程产生影响。采用主分量分析算法提取去噪信号的特征,并根据遗传算法优化支持向量参数,将提取的特征输入到向量机中,通过特征的分类完成通信干扰下无线传感器网络微弱信号的检测。实验结果表明,所提方法的信号检测结果与实际结果基本一致,检测时间在30ms内,且抗噪性能强。

抗差Helmert分量估计在地铁CPⅢ控制网平差中的应用

为使CPⅢ控制网数据定权合理、提高平差成果精度,并具有抗粗差特性,论文提出采用结合Huber的Helmert抗差分量估计定权方法对CPⅢ控制网中的水平角、竖直角和斜距三类观测值进行合理定权平差,以福州市轨道交通4号线工程西门站至东街口站区间右线CPⅢ控制网测量项目为例,验证了该方法的定权合理性和可靠性,并可获得点位偏差小、精度可靠且有抗粗差能力的平差成果。

基于方差分量估计的视觉定位方法

针对视觉导航中存在成像特征点的非均匀误差会影响位姿计算准确度的问题,提出一种基于方差-协方差分量估计的视觉定位方法。首先,从理论角度出发分析相机测量过程中随机误差存在形式及影响因素;其次,向双目相机视觉及视觉/惯性融合模型添加随机模型优化模块,构建非线性优化系统;最后,使用KITTI数据集、EuRoC数据集进行测试,对数据进行基于划分的聚类处理,验证得到该方法能够更加有效精准地估计飞行目标的位姿信息,对随机误差具有良好的优化效果。

《史记》《汉书》“论赞序”语气类成分及相关句式比较研究

以《史记》和《汉书》的“论赞序”为考察对象,全面统计比较两者在语气类成分(语气词、叹词、表语气组合)上的使用差别。综合来看,《史记》的语气类成分较之《汉书》在使用类型和总数上都要丰富,而且几类表言说主观性的语气词使用数量上差异明显。同时,语气词往往和句首词组成表各类语气情态的句式,两书在这类句法格式上也存在颇多不同。语气类成分使用的不同当为两书维持语言主、客观性的手段。