共查询到20条相似文献,搜索用时 140 毫秒
1.
杜世平 《纯粹数学与应用数学》2008,24(3)
对隐Maxkov模型(hidden Markov model:HMM)的状态驻留时间的概率进行了修订,给出了改进的带驻留时间隐Markov模型的结构,并在传统的隐Markov模型(traditional hidden Markov model:THMM)的基础上讨论了新模型的前向.后向变量,导出了新模型的前向-后向算法的迭代公式,同时也给出了新模型各个参数的重估公式. 相似文献
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利用隐函数的导数和矩阵正定性在多元显函数极值方面的应用,给出隐函数极值存在的必要条件和充分条件,并实例说明如何根据矩阵的正定性判定隐函数的极值. 相似文献
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用MAOR迭代算法求解一类L-矩阵的隐线性互补问题.证明了由此算法产生的迭代序列的聚点是隐线性互补问题的解.并且当问题中的矩阵是M-矩阵时,算法产生的迭代序列单调收敛于隐互补问题的解. 相似文献
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探讨隐变量交互作用分析的建模方法及其在SA S软件上的实现;首先阐述隐变量交互作用分析建模的原理,通过拟合一个隐变量交互作用分析示例,对加入交互作用项的统计分析结果与无交互作用项的统计分析结果进行对比;示例中交互作用项引起的变异在结果变量的总变异中占到79.20%.在应用结构方程模型分析隐变量产生的效应时,有时候不进行隐变量交互作用的分析可能会产生偏倚. 相似文献
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内隐追随是西方管理学中的一个新兴研究议题。相对于西方已有的内隐追随维度研究,以及我国学者对内隐追随结构维度的本土化研究,目前以高等学校为研究背景,以高校科研团队领导为研究对象的内隐追随结构维度的研究还较为匮乏。基于KAQ理论,在归纳高校科研团队领导者内隐追随特征的基础上,提出其结构维度的理论框架,并利用SPSS 20.0和Mplus7.4分别对高校科研团队内隐追随结构维度进行探索性和验证性因素分析,最后,对所开发的量表进行预测效度和增量效度的检验。结果表明,高校科研团队领导内隐追随包括原型和反原型两大维度,每个维度还包括知识、能力、素质原型与反原型六个子维度。同时,也验证了该量表具有较好的预测效度和增量效度。最后,阐述结论、研究贡献以及研究的局限性和未来研究的展望,以供高校领导发展计划领域研究学者参考。 相似文献
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参数曲线的分段近似隐式化 总被引:1,自引:0,他引:1
曲线的参数形式和隐式形式之间的相互转化在CAGD和造型中起着重要的作用,将参数形式的曲线转化为代数形式的曲线的过程称为隐式化.由于复杂参数曲线的精确隐式化比较困难,因此近似隐式化就显得非常重要.基于隐函数样条与异度隐函数样条,作者提出了参数曲线分段近似隐式化方法,并给出具体算法.通过实例对比,说明了此方法的简洁性和有效性. 相似文献
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Nariaki Sugiura 《Journal of multivariate analysis》1976,6(4):500-525
Asymptotic expansions of the joint distributions of the latent roots of the Wishart matrix and multivariate F matrix are obtained for large degrees of freedom when the population latent roots have arbitrary multiplicity. Asymptotic expansions of the distributions of the latent vectors of the above matrices are also derived when the corresponding population root is simple. The effect of normalizations of the vector is examined. 相似文献
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We investigate the structure of a large precision matrix in Gaussian graphical models by decomposing it into a low rank component and a remainder part with sparse precision matrix.Based on the decomposition,we propose to estimate the large precision matrix by inverting a principal orthogonal decomposition(IPOD).The IPOD approach has appealing practical interpretations in conditional graphical models given the low rank component,and it connects to Gaussian graphical models with latent variables.Specifically,we show that the low rank component in the decomposition of the large precision matrix can be viewed as the contribution from the latent variables in a Gaussian graphical model.Compared with existing approaches for latent variable graphical models,the IPOD is conveniently feasible in practice where only inverting a low-dimensional matrix is required.To identify the number of latent variables,which is an objective of its own interest,we investigate and justify an approach by examining the ratios of adjacent eigenvalues of the sample covariance matrix?Theoretical properties,numerical examples,and a real data application demonstrate the merits of the IPOD approach in its convenience,performance,and interpretability. 相似文献
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Chi-Ming Tsou 《Applied Mathematical Modelling》2012,36(12):6154-6166
A data analysis method is proposed to derive a latent structure matrix from a sample covariance matrix. The matrix can be used to explore the linear latent effect between two sets of observed variables. Procedures with which to estimate a set of dependent variables from a set of explanatory variables by using latent structure matrix are also proposed. The proposed method can assist the researchers in improving the effectiveness of the SEM models by exploring the latent structure between two sets of variables. In addition, a structure residual matrix can also be derived as a by-product of the proposed method, with which researchers can conduct experimental procedures for variables combinations and selections to build various models for hypotheses testing. These capabilities of data analysis method can improve the effectiveness of traditional SEM methods in data property characterization and models hypotheses testing. Case studies are provided to demonstrate the procedure of deriving latent structure matrix step by step, and the latent structure estimation results are quite close to the results of PLS regression. A structure coefficient index is suggested to explore the relationships among various combinations of variables and their effects on the variance of the latent structure. 相似文献
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Variations of the latent semantic indexing (LSI) method in information retrieval (IR) require the computation of singular subspaces associated with the k dominant singular values of a large m × n sparse matrix A, where k?min(m,n). The Riemannian SVD was recently generalized to low‐rank matrices arising in IR and shown to be an effective approach for formulating an enhanced semantic model that captures the latent term‐document structure of the data. However, in terms of storage and computation requirements, its implementation can be much improved for large‐scale applications. We discuss an efficient and reliable algorithm, called SPK‐RSVD‐LSI, as an alternative approach for deriving the enhanced semantic model. The algorithm combines the generalized Riemannian SVD and the Lanczos method with full reorthogonalization and explicit restart strategies. We demonstrate that our approach performs as well as the original low‐rank Riemannian SVD method by comparing their retrieval performance on a well‐known benchmark document collection. Copyright 2004 John Wiley & Sons, Ltd. 相似文献
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Asymptotic distributions of the latent roots of the covariance matrix with multiple population roots
Yasuko Chikuse 《Journal of multivariate analysis》1976,6(2):237-249
An asymptotic expansion for large sample size n is derived by a partial differential equation method, up to and including the term of order n?2, for the 0F0 function with two argument matrices which arise in the joint density function of the latent roots of the covariance matrix, when some of the population latent roots are multiple. Then we derive asymptotic expansions for the joint and marginal distributions of the sample roots in the case of one multiple root. 相似文献
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Sadanori Konishi Takakazu Sugiyama 《Annals of the Institute of Statistical Mathematics》1981,33(1):27-33
Summary Normalizing transformations of the largest and the smallest latent roots of a sample covariance matrix in a normal sample
are obtained, when the corresponding population roots are simple. Using our results, confidence intervals for population roots
may easily be constructed. Some numerical comparisons of the resulting approximations are made in a bivariate case, based
on exact values of the probability integral of latent roots. 相似文献
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本文研究了与矩阵Γ分布相关的若干分布的密度函数,利用矩阵Γ分布的特征函数和它的Bartlett分解等方法,获得了与矩阵Γ分布相关的几个分布的密度函数解析表达式,它们包括Γ分布随机矩阵的子矩阵、行列式、迹和特征根的分布密度,进一步还得到了相关系数矩阵的分布密度函数形式. 相似文献
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Robb J. Muirhead 《Journal of multivariate analysis》1975,5(3):283-293
Reasonably simple expressions are given for some hypergeometric functions when the size of the argument matrix or matrices is two. Applications of these expressions in connection with the distributions of the latent roots of a 2 × 2 Wishart matrix are also given. 相似文献
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Hiroyuki Uesaka Chooichiro Asano 《Annals of the Institute of Statistical Mathematics》1987,39(1):513-531
Summary The purpose of the present paper is to propose an analytical method for ordered categorical responses obtained from a repeated
measurement/longitudinal experiment. The ordered categorical scale is assumed to be a manifestation of a latent quantitative
variable. A linear model is assumed for location parameters of the underlying distributions. Weighted least square method
is applied to parameter estimation and subsequent analysis. Two data sets are analyzed to show several aspects of analysis
by the proposed model and to discuss comparative characteristics of analysis compared with earlier analysis. A mention is
made for a computer software program for the proposed model. 相似文献
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Delores Conway 《Statistics & probability letters》1982,1(2):103-106
The maximum entropy covariance matrix is positive definite even when the number of variables p exceeds the sample size n. However, the inverse of this matrix can have stability problems when p is close to n, although these problems tend to disappear as p increases beyond n. We analyze such problems using the variance of the latent roots in a particular metric as a condition number. 相似文献