AR（P）参数估计协方差方法与其改进型方法的对比研究     

楚岩  祝轩 《地球科学与环境学报》1997,(Z1)

定性分析协方差方法及其改进型在ＡＲ（Ｐ）参数估计中的应用，提供了一种估计参数的计算机上机流程图，比较了协方差方法及其改进型谱估计结果。  相似文献   

棉兰老涡旋的数值模拟     

李荣凤  曾庆存  季仲贞 《热带海洋学报》1991,(4)

用一个水平分辨率比较高的非线性正压模式,对热带西太平洋海域里的海流进行了数值模拟。计算海流与观测结果颇相一致。结果表明:赤道以北、棉兰老岛以东附近海域中,棉兰老冷涡在一年的大部分时间里都得到发展,但是其范围、强度和中心位置在各个月份并不相同,显示出很强的季节变化;在哈马黑拉岛东北海域,6月份突然出现了一个反气旋式的涡旋;这个涡旋8月份发展得最强,一直持续到10月份,在11月份就消亡了。  相似文献   

吕宋冷涡时空特征概况   总被引：1，自引：0，他引：1  

刘金芳  毛可修  闫明  张绪东  石永军 《海洋预报》2006,23(2):39-44

本文在收集南海以及台湾周边海域1900~2004年共105a的海洋调查资料,按0.5°×0.5°网格进行温盐计算统计分析,制作了逐月平面、断面分布图的基础上,参考相关文献和海洋水文图集,阐明了吕宋冷涡的空间、季节特点及其变化规律。中尺度涡对海洋科学、舰艇航行及国防建设等都有重要的学术意义和实际应用意义。  相似文献   

秋季东海黑潮锋面涡旋的特点     

郑义芳  黄卫民 《海洋科学进展》1994,(3)

本文根据１９８８年１０月２０～２４日“向阳红０９号”调查船在奄美大岛以西海区进行调查时，在陆架斜坡上的表均温层的底部，即５０ｍ附近的深层上，出现一个黑潮锋面涡。不论在平面上形成的特点，还是在断面上水系配置的形式，它与春季黑潮锋面涡的特征极为相似。说明秋季东海同样存在陆架水与黑潮水在水平方向交换与混合。  相似文献   

Empirical weighting of multiple stock-abundance indices for parameter estimation and stock assessment in a multi-zone or multi-species fishery     

McDonald  A. David; Kendrick  Terese H.; Breen  Paul A. 《ICES Journal of Marine Science》2001,58(1):204-215

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协方差阵二次型容许估计的一个必要条件     

张立振  徐兴忠 《中国海洋大学学报(自然科学版)》2002,32(2):325-328

研究协方差阵Σ的二次型容许估计问题。设 y1,y2 ,… ,yniid,n≥ 2 ,y1与 p维正态分布N (β,Σ )有相同的前四阶矩。其中β =(β1,β2 ,… ,βp)′∈ Rp与Σ =(σij) p× p >0均未知。记 y =△ (y1,y2 ,… ,yn)′。在二次损失 L (d ,Σ ) =tr(d -Σ) 2下给出Σ的二次型估计 a S2 + nby-y-′是容许估计的必要条件为 :(n - 1) a + b + 2 max(a,b)≤ 1。此必要条件比张立振等协方差阵的二次型容许估计中的必要条件有了明显的加强  相似文献   

西太平洋暖池区气象学特征分析   总被引：1，自引：0，他引：1  

吴增茂  张庆荣 《海洋湖沼通报》1994,(2):113-119

本文根据ＴＯＧＡ－ＣＯＡＲＥ强化观测资料及卫星资料分析讨论了西太平洋暖池区１１－２月间海面气象要素的平均特征。分析发现海面气压平均日变化似乎同中纬度区明显不同。文中还分析讨论了在西太暖池区西风爆发、赤道浅涡、信风逆温及ＩＴＣＥ和南太平洋辐合带ＳＰＣＺ的变化特征。  相似文献   

抗差最小二乘配置在数字化地图几何纠正中的应用讨论     

张亮 《测绘与空间地理信息》2008,31(2):145-148

抗差估计具有较好的抗拒异常观测值及粗差的能力,而最小二乘配置又能较好地处理系统误差,本文结合两者的优点,利用抗差最小二乘配置对数字化地图进行几何纠正,其中对协方差函数采用抗差拟合,得到了较好的结果。实验证明在GIS数据处理的扫描数字化地图几何纠正中,抗差最小二乘配置在抗拒异常值和处理系统误差方面优于单纯的最小二乘估计和单纯的最小二乘配置方法。  相似文献   

Estimation of covariance matrix from geochemical data with observations below detection limits     

Chang-Jo F. Chung 《Mathematical Geology》1993,25(7):851-865

Multivariate statistical analyses have been extensively applied to geochemical measurements to analyze and aid interpretation of the data. Estimation of the covariance matrix of multivariate observations is the first task in multivariate analysis. However, geochemical data for the rare elements, especially Ag, Au, and platinum-group elements, usually contain observations the below detection limits. In particular, Instrumental Neutron Activation Analysis (INAA) for the rare elements produces multilevel and possibly extremely high detection limits depending on the sample weight. Traditionally, in applying multivariate analysis to such incomplete data, the observations below detection limits are first substituted, for example, each observation below the detection limit is replaced by a certain percentage of that limit, and then the standard statistical computer packages or techniques are used to obtain the analysis of the data. If a number of samples with observations below detection limits is small, or the detection limits are relatively near zero, the results may be reasonable and most geological interpretations or conclusions are probably valid. In this paper, a new method is proposed to estimate the covariance matrix from a dataset containing observations below multilevel detection limits by using the marginal maximum likelihood estimation (MMLE) method. For each pair of variables, sayY andZ whose observations containing below detection limits, the proposed method consists of three steps: (i) for each variable separately obtaining the marginal MLE for the means and the variances, , , , and forY andZ: (ii) defining new variables by and and lettingA=C+D andB=C–D, and obtaining MLE for variances, and forA andB; (iii) estimating the correlation coefficient _YZ by and the covariance _YZ by . The procedure is illustrated by using a precious metal geochemical data set from the Fox River Sill, Manitoba, Canada.  相似文献   

Size Scaling of Cross Correlation Between Multiple Variables     

Oz  Bora  Deutsch  Clayton V. 《Natural Resources Research》2002,11(1):1-18

Reservoir models have large uncertainty because of spatial variability and limited sample data. The ultimate aim is to use simultaneously all available data sources to reduce uncertainty and provide reliable reservoir models for resource assessment and flow simulation. Seismic impedance or some other attribute provides a key source of data for reservoir modeling. These seismic data are at a coarser scale than the hard well data and it not an exact measurement of facies proportions or porosity. A requirement for data integration is the cross-covariance between the well and seismic data.The size-scaling behavior of the cross correlation for different measurement scales was nvestigated. The size-scaling relationship is derived theoretically and validated by numerical studies (including an example with real data). The limit properties of the cross-correlation coefficient when the averaging volume becomes large is shown. After some averaging volume, the volume-dependent cross-correlation coefficient reaches a limit value. This plateau value is controlled mainly by the large-scale behavior of the cross and direct variograms.The cross correlation can increase or decrease with volume support depending on the relative importance of long- and short-scale covariance structures. If the direct and cross variograms are proportional, there is no change in the cross correlation as the averaging volume changes. Our study shows that the volume-dependent cross-correlation coefficient is sensitive to the shape of the cross variogram and differences between the direct variograms of the well data and seismic data.  相似文献