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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...
Testing the Diagonality of a Large Covariance Matrix in a Regression Setting
Bias-Corrected Test Covariance Diagonality Test High Di- mensional Data
2016/1/26
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...
Band Width Selection for High Dimensional Covariance Matrix Estimation
Bandable covariance Banding estimator Large p, small n Ratio- consistency Tapering estimator Thresholding estimator
2016/1/25
The banding estimator of Bickel and Levina (2008a) and its tapering version of Cai, Zhang and Zhou (2010), are important high dimensional covariance esti-mators. Both estimators require choosing a ban...
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...
Band Width Selection for High Dimensional Covariance Matrix Estimation
Bandable covariance Banding estimator Large p small n
2016/1/20
The banding estimator of Bickel and Levina (2008a) and its tapering version of Cai, Zhang and Zhou (2010), are important high dimensional covariance esti-mators. Both estimators require choosing a ban...
Subspaces that Minimize the Condition Number of a Matrix
Subspaces Minimize Condition Number Matrix
2015/7/9
We define the condition number of a nonsingular matrix on a subspace, and consider the problem of finding a subspace of given dimension that minimizes the condition number of a given matrix. We give a...
Estimation of an Origin/Destination matrix: Application to a ferry transport data
constraint maximum likelihood estimation eigenvectors counts estimation
2013/6/14
The estimation of the number of passengers with the identical journey is a common problem for public transport authorities. This problem is also known as the Origin- Destination estimation (OD) proble...
Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization
Structural Functional Discovery Dynamic Networks Non-negative Matrix Factorization
2013/6/17
Time series of graphs are increasingly prevalent in modern data and pose unique challenges to visual exploration and pattern extraction. This paper describes the development and application of matrix ...
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Parallel Gaussian Process Regression Low-Rank Covariance Matrix Approximations
2013/6/14
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due ...
Stable Estimation of a Covariance Matrix Guided by Nuclear Norm Penalties
Covariance estimation Regularization Condition number Canonical correlation analysis Discriminant analysis Clustering
2013/6/14
Estimation of covariance matrices or their inverses plays a central role in many statistical methods. For these methods to work reliably, estimated matrices must not only be invertible but also well-c...
CLT for linear spectral statistics of random matrix $S^{-1}T$
CLT linear spectral statistics random matrix $S^{-1}T$
2013/6/13
This paper proposes a CLT for linear spectral statistics of random matrix $S^{-1}T$ for a general non-negative definite and {\bf non-random} Hermitian matrix $T$.
A Note on Central Limit Theorems for Linear Spectral Statistics of Large Dimensional F-matrix
Linear spectral statistics central limit theorem centralized sample covari-ance matrix centralizedF-matrix simplified sample covariance matrix simplified F-matrix
2013/6/13
Sample covariance matrix and multivariate $F$-matrix play important roles in multivariate statistical analysis. The central limit theorems {\sl (CLT)} of linear spectral statistics associated with the...
Estimating the quadratic covariation matrix from noisy observations: local method of moments and efficiency
adaptive estimation asymptotic equivalence asynchronous ob-servations integrated covolatility matrix quadratic covariation semiparametric eciency,microstructure noise spectral estimation
2013/4/28
An efficient estimator is constructed for the quadratic covariation or integrated covolatility matrix of a multivariate continuous martingale based on noisy and non-synchronous observations under high...
Generalizing k-means for an arbitrary distance matrix
Generalizing k-means an arbitrary distance matrix
2013/5/2
The original k-means clustering method works only if the exact vectors representing the data points are known. Therefore calculating the distances from the centroids needs vector operations, since the...