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Spatial Weights Matrix Selection and Model Averaging for Spatial Autoregressive Models
Model Selection Model Averaging Spatial Econometrics Spatial Autoregressive
2016/1/26
Spatial econometrics relies on spatial weights matrix to specify the cross sectional depen-dence, which might not be unique. This paper proposes a model selection procedure to choose an optimal weight...
Spatial Weights Matrix Selection and Model Averaging for Spatial Autoregressive Models
Model Selection Model Averaging Spatial Econometrics Spatial Autoregressive
2016/1/20
Spatial econometrics relies on spatial weights matrix to specify the cross sectional depen-dence, which might not be unique. This paper proposes a model selection procedure to choose an optimal weight...
Testing the Diagonality of a Large Covariance Matrix in a Regression Setting
Bias-Corrected Test Covariance Diagonality Test High Di- mensional Data Multivariate Analysis
2016/1/20
In multivariate analysis, the covariance matrix associated with a set of vari-ables of interest (namely response variables) commonly contains valuable infor-mation about the dataset. When the dimensio...
Random matrix approach to the dynamics of stock inventory variations
Random matrix approach dynamics stock inventory variations
2012/3/2
We study the cross-correlation matrix $C_{ij}$ of inventory variations of the most active individual and institutional investors in an emerging market to understand the dynamics of inventory variation...
This work introduces SubMF, a parallel divide-and-conquer framework for noisy matrix factorization.
Dynamic Large Spatial Covariance Matrix Estimation in Application to Semiparametric Model Construction via Variable Clustering: the SCE approach
Time Series Covariance Estimation Regularization, Sparsity
2011/7/6
To better understand the spatial structure of large panels of economic and financial time series and provide a guideline for constructing semiparametric models, this paper first considers estimating a...
A Generalized Least Squares Matrix Decomposition
matrix decomposition,singular value decomposition,transposable data,principal components analysis,sparse principal components analysis,functional prin-cipal components analysis,spatio-temporal data
2011/3/21
Variables in high-dimensional data sets common in neuroimaging, spatial statistics, time series and genomics often exhibit complex dependencies. Conventional multivariate analysis techniques often ign...
A Generalized Least Squares Matrix Decomposition
matrix decomposition singular value decomposition transposable data principal components analysis, sparse principal components analysis functional prin-cipal components analysis spatio-temporal data
2011/3/23
Variables in high-dimensional data sets common in neuroimaging, spatial statistics, time series and genomics often exhibit complex dependencies. Conventional multivariate analysis techniques often ign...
When a matrix A with n columns is known to be well approximated by a linear combination of basis matrices B_1,..., B_p, we can apply A to a random vector and solve a linear system to recover this line...
Sparse Bayesian Methods for Low-Rank Matrix Estimation
Low-Rank Matrix Estimation Sparse Bayesian Methods
2011/3/24
Recovery of low-rank matrices has recently seen significant activity in many areas of science and engineering, motivated by recent theoretical results for exact reconstruction guarantees and interesti...
Adaptive Thresholding for Sparse Covariance Matrix Estimation
constrained ℓ 1 minimization covariance matrix Frobenius norm Gaus-sian graphical model rate of convergence precision matrix spectral norm
2011/3/21
In this paper we consider estimation of sparse covariance matrices and propose a thresholding procedure which is adaptive to the variability of individual entries. The estimators are fully data driven...
A Constrained L1 Minimization Approach to Sparse Precision Matrix Estimation
constrained ℓ 1 minimization covariance matrix Frobenius norm Gaus-sian graphical model rate of convergence precision matrix spectral norm
2011/3/21
A constrained L1 minimization method is proposed for estimating a sparse inverse covariance matrix based on a sample of $n$ iid $p$-variate random variables. The resulting estimator is shown to enjoy ...