[PDF] Top 20 High-dimensional Statistical Inference: from Vector to Matrix
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High-dimensional Statistical Inference: from Vector to Matrix
... modern high-dimensional statistics, different kinds of structural assumptions have been imposed on the ...range from linear regression coefficient to the covariance structure or network, ...in ... See full document
247
Singular Value Decomposition for High Dimensional Data
... the high dimensional setting, statistical estimation is not possible without the assumption of strong structure in the ...for vector data un- der Gaussian sequence models (Johnstone, 2011), ... See full document
106
Local Curvature and Centering Effects in Nonlinear Regression Models
... residual vector, an important aspect of the linear argument above, when there is intrinsic curvature present, the usual geometric properties of the residual vector are affected as they are the projection of ... See full document
9
FCM BPSO: ENERGY EFFICIENT TASK BASED LOAD BALANCING IN CLOUD COMPUTING
... In this study, we modified the method of Two Dimensional Linear Discriminant Analysis (Modified 2DLDA). This 2DLDA modification method can directly assess without transformation matrix image into ... See full document
6
A Direct Estimation of High Dimensional Stationary Vector Autoregressions
... the vector autoregressive model is an interesting problem and has been in- vestigated for a long ...However, high dimensionality brings significantly new challenges and viewpoints to this classic ... See full document
36
V-Matrix Method of Solving Statistical Inference Problems
... The significant difference, however, is that both classical models were developed for the known (fixed) vectors X, while V -matrix is defined for random vectors X and is computed using these vectors. It takes into ... See full document
48
Band Width Selection for High Dimensional Covariance Matrix Estimation
... away from the diagonal, Bickel and Levina (2008b) proposed a thresholding estimator, which was further developed by Rothman, Levina and Zhu (2009) and Cai and Liu ... See full document
36
Recurrent Kalman networks:factorized inference in high dimensional deep feature spaces
... any matrix M, M ˆ denotes a diagonal matrix with the same diagonal as M, m denotes a vector containing those diagonal elements and M (ij) denotes the entry at row i and column ... See full document
6
Contributions to Statistical Methods for High Dimensional and Dependent Data.
... gradient vector and Hessian matrix of the hinge loss, see Lemma 2 and Lemma 3 of Koo et ...on high dimensional inference (Kim et ...design matrix in (A5) is similar to the sparse ... See full document
139
Statistical Inference For High-Dimensional Linear Models
... the statistical inference problem in the high-dimensional instrumental variable framework with possibly invalid ...general inference procedure that provides honest inference in ... See full document
253
Recurrent Kalman networks:factorized inference in high dimensional deep feature spaces
... to high dimensional latent states, whether they can be used for state estimation, whether they can provide uncertainty estimates, whether they can handle noise or miss- ing data, and whether the objective ... See full document
9
Variational Bayes inference in high dimensional time varying parameter models
... results from a simple benchmark such as rolling ...parameter vector is sparse, in which case procedures such as (unrestricted) rolling OLS are condemned to be over-parameterized and not track coefficients ... See full document
61
The Factor Lasso and K Step Bootstrap Approach for Inference in High Dimensional Economic Applications
... rich high-dimensional data offers many opportunities for empirical re- searchers but also poses statistical challenges in that regularization or dimension reduction will generally be needed for ... See full document
82
Eigenvalue regularized covariance matrix estimators for high dimensional data
... covariance matrix or its inverse, called the precision matrix, is an important and sometimes inevitable task in data ...range from risk estimation and portfolio allocation in finance to ... See full document
175
Efficient Learning and Inference for High-dimensional Lagrangian Systems
... Figure 9.4 shows the three-dimensional basis learned for a specific instance of this problem consisting of a maze-like environment. As would be expected, the first two basis vectors encode just the position of the ... See full document
143
Selection and Inference for High-Dimensional Regression with Applications in Biomedical Research.
... the statistical inference for high-dimensional regres- sion based upon lasso solution ...shows high false positive proportion in our simulation for high-dimensional ... See full document
123
Statistical Methods for High-Dimensional, Spatially-Distributed Microbiome Data from Next-Generation Sequencing.
... collected from across the continental ...taxa from these samples, many of which exhibit a high degree of geographic ...with high probability. In addition, our statistical approach ... See full document
103
Bayesian Inference for High Dimensional Models: Convergence Properties and Computational Issues.
... Chakrabarti from RKM Narendrapur for showing the path of effectively teaching Statistics at the undergraduate ...a statistical problem still remain a benchmark for me to follow as a teacher in the ... See full document
136
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models
... on statistical inference for graphical ...of high dimensional graphical model estimation ...existing high dimensional inference methods are specifically designed for ... See full document
78
Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High Dimensional Features
... Our contributions in this paper are two-fold. First, we propose a novel model for biomedical event extraction based on MLNs that addresses the aforementioned limitations by leveraging the power of Support Vector ... See full document
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