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   

海浪的条件特征周期     

徐德伦  刘学海  张军 《海洋学报》2001,23(3):1-7

在海浪波面高度为正态分布的假定下,导出一种以给定波高为条件的条件周期概率密度函数.与风浪槽中测量数据比较,结果表明,在窄谱情况下此概率密度函数与实验室风浪的实际符合良好.根据此密度函数定义了3种条件特征周期,并导出它们与平均周期的关系式.根据这些关系对有关海洋工程上的一些问题作了解释和讨论.  相似文献   

浙江西沪港重金属铜的配位容量和形态分析   总被引：3，自引：2，他引：3       下载免费PDF全文

吕海燕  王正方  傅和芳  冯旭文 《海洋学报》2003,25(3):47-52

根据2000年6月10～14日在浙江省西沪港采集的海水样品,利用AA-800石墨炉原子吸收分光光度技术和阳极溶出伏安法测定样品中重金属铜的含量,获得铜在海水中受不同的有机配体控制.不同粒级的铜表观配位容量表明西沪港海水过孔径1.00μm微孔膜的(ACuCC)较高,为144.4nmol/dm3;过0.40和0.20μm滤膜的(ACuCC)分别为103.0和102nmol/dm3;铜的有机配体条件稳定常数的对数值在7.25～9.14之间.铜的总量为21.72nmol/dm3.铜全部为稳定溶解态,其中pH2酸溶态占95.0%,强有机结合态占5.0%.溶解态铜中有机结合态占过滤海水中总铜的61.6%.  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   

极谱法测定海水介质的铜络合容量     

张怀成  孙秉一  史致丽  桓新 《中国海洋大学学报(自然科学版)》1990,(4)

采用新极谱技术(1.5次微分)的阳极溶出伏安滴定方法测定天然海水的铜络合容量和条件稳定常数。对测试条件、EDTA回收率和有关问题进行了探讨。青岛近岸水样的九次平行测定表明,铜的表现络合容量为3.01×10~(-7)mol/dm~3,条件稳定常数为7.34×10~7,相对标准偏差分别为9.2％和13.8％。  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   

Evaluation of the Reduction in Uncertainty Obtained by Conditioning a 3D Stochastic Channel to Multiwell Pressure Data     

Fengjun Zhang  Albert C. Reynolds  Dean S. Oliver 《Mathematical Geology》2002,34(6):715-742

A stochastic channel embedded in a background facies is conditioned to data observed at wells. The background facies is a fixed rectangular box. The model parameters consist of geometric parameters that describe the shape, size, and location of the channel, and permeability and porosity in the channel and nonchannel facies. We extend methodology previously developed to condition a stochastic channel to well-test pressure data, and well observations of the channel thickness and the depth of the top of the channel. The main objective of this work is to characterize the reduction in uncertainty in channel model parameters and predicted reservoir performance that can be achieved by conditioning to well-test pressure data at one or more wells. Multiple conditional realizations of the geometric parameters and rock properties are generated to evaluate the uncertainty in model parameters. The ensemble of predictions of reservoir performance generated from the suite of realizations provides a Monte Carlo estimate of the uncertainty in future performance predictions. In addition, we provide some insight on how prior variances, data measurement errors, and sensitivity coefficients interact to determine the reduction in model parameters obtained by conditioning to pressure data and examine the value of active and observation well data in resolving model parameters.  相似文献   

Combination of Dependent Realizations Within the Gradual Deformation Method     

Lin Y. Hu 《Mathematical Geology》2002,34(8):953-963

Gradual deformation is a parameterization method that reduces considerably the unknown parameter space of stochastic models. This method can be used in an iterative optimization procedure for constraining stochastic simulations to data that are complex, nonanalytical functions of the simulated variables. This method is based on the fact that linear combinations of multi-Gaussian random functions remain multi-Gaussian random functions. During the past few years, we developed the gradual deformation method by combining independent realizations. This paper investigates another alternative: the combination of dependent realizations. One of our motivations for combining dependent realizations was to improve the numerical stability of the gradual deformation method. Because of limitations both in the size of simulation grids and in the precision of simulation algorithms, numerical realizations of a stochastic model are never perfectly independent. It was shown that the accumulation of very small dependence between realizations might result in significant structural drift from the initial stochastic model. From the combination of random functions whose covariance and cross-covariance are proportional to each other, we derived a new formulation of the gradual deformation method that can explicitly take into account the numerical dependence between realizations. This new formulation allows us to reduce the structural deterioration during the iterative optimization. The problem of combining dependent realizations also arises when deforming conditional realizations of a stochastic model. As opposed to the combination of independent realizations, combining conditional realizations avoids the additional conditioning step during the optimization process. However, this procedure is limited to global deformations with fixed structural parameters.  相似文献