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含离散节点协变量的大型随机区块模型谱估计AI 解读

In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure. We show that a network model with discrete unobserved link heterogeneity and binary (or discrete) covariates corresponds to a stochastic blockmodel (SBM). We develop a spectral estimator for the effect of covariates on link probabilities, exploiting the correspondence of SBMs and generalized random dot product graphs (GRDPG). We show…

谱嵌入的统计解释：广义随机点积图AI 解读

Abstract Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph. This paper proposes a generalisation of the latent position network model known as the random dot product graph, to allow interpretation of those vector representations as latent position estimates. The generalisation is needed to model heterophilic connectivity (e.g. ‘opposites attract’) and to cope with negative eigenvalues more generally. We show that,…

从距离相关到多尺度图相关AI 解读

Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age. In this paper, we establish a new framework that generalizes distance correlation (Dcorr)—a correlation measure that was recently proposed and shown to be universally consistent for dependence testing against all joint distributions of finite moments—to the…

二到无穷范数与奇异子空间几何及其高维统计应用AI 解读

The singular value matrix decomposition plays a ubiquitous role throughout statistics and related fields. Myriad applications including clustering, classification, and dimensionality reduction involve studying and exploiting the geometric structure of singular values and singular vectors. This paper provides a novel collection of technical and theoretical tools for studying the geometry of singular subspaces using the two-to-infinity norm. Motivated by preliminary…

随机图归一化拉普拉斯矩阵特征向量的极限定理AI 解读

We prove a central limit theorem for the components of the eigenvectors corresponding to the $d$ largest eigenvalues of the normalized Laplacian matrix of a finite dimensional random dot product graph. As a corollary, we show that for stochastic blockmodel graphs, the rows of the spectral embedding of the normalized Laplacian converge to multivariate normals and, furthermore, the mean and the covariance matrix of each row are functions of the associated vertex’s block…

通过 EM 算法实现最大 Lq 似然的混合模型稳健估计AI 解读

We introduce a maximum Lq-likelihood estimation (MLqE) of mixture models using our proposed expectation-maximization (EM) algorithm, namely the EM algorithm with Lq-likelihood (EM-Lq). Properties of the MLqE obtained from the proposed EM-Lq are studied through simulated mixture model data. Compared with the maximum likelihood estimation (MLE), which is obtained from the EM algorithm, the MLqE provides a more robust estimation against outliers for small sample sizes. In…

潜在位置图上的普遍相合顶点分类AI 解读

In this work we show that, using the eigen-decomposition of the adjacency matrix, we can consistently estimate feature maps for latent position graphs with positive definite link function $\kappa$, provided that the latent positions are i.i.d. from some distribution $F$. We then consider the exploitation task of vertex classification where the link function $\kappa$ belongs to the class of universal kernels and class labels are observed for a number of vertices tending to…

随机块模型图的一致邻接谱嵌入AI 解读

We present a method to estimate block membership of nodes in a random graph generated by a stochastic blockmodel. We use an embedding procedure motivated by the random dot product graph model, a particular example of the latent position model. The embedding associates each node with a vector; these vectors are clustered via minimization of a square error criterion. We prove that this method is consistent for assigning nodes to blocks, as only a negligible number of nodes…

混合模型复杂度的一致估计AI 解读

The consistent estimation of mixture complexity is of fundamental importance in many applications of finite mixture models. An enormous body of literature exists regarding the application, computational issues and theoretical aspects of mixture models when the number of components is known, but estimating the unknown number of components remains an area of intense research effort. This article presents a semiparametric methodology yielding almost sure convergence of the…

基于随机扫描分区的空间扫描统计量AI 解读

This article develops a spatial scan statistic for homogeneity analysis of point processes that utilizes stochastic scan partitions. The derivation of the sampling distribution for the statistic yields an exact test. This test has the potential for improved power over conventional alternatives when the point process is embedded in an underlying continuous random field and is recommended in situations for which the location of subregions of nonhomogeneity in the point process…

借用信息的非同质性分析AI 解读

This article develops a “borrowed strength” methodology for estimating local probability densities in a random field. Under a piecewise stationarity condition similarities between the densities in different regions of the field can be exploited by estimating a global mixture density and imposing the parameter-space support of this borrowed strength estimate on the local estimation problems. The local estimates are then used in an analysis of the homogeneity of the field,…

借力估计的非齐性分析AI 解读

This article develops a “borrowed strength” methodology for estimating local probability densities in a random field. Under a piecewise stationarity condition similarities between the densities in different regions of the field can be exploited by estimating a global mixture density and imposing the parameter-space support of this borrowed strength estimate on the local estimation problems. The local estimates are then used in an analysis of the homogeneity of the field,…

自适应混合密度估计AI 解读

The estimation of a probability density function based on a sample of independent identically distributed observations is essential in a wide range of applications. In particular, a sequence of estimates that converges in some sense to the true density α 0 can yield asymptotically optimal performance in classification and discrimination problems. In this article an estimation technique called “adaptive mixtures” is developed from the related methods of kernel estimation and…