搜索结果: 1-14 共查到“统计核算理论 Q-matrix”相关记录14条 . 查询时间(0.119 秒)
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 $T$.
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...
$l_{2,p}$ Matrix Norm and Its Application in Feature Selection
$l_{2,p}$ Matrix Norm Its Application Feature Selection
2013/5/2
Recently, $l_{2,1}$ matrix norm has been widely applied to many areas such as computer vision, pattern recognition, biological study and etc. As an extension of $l_1$ vector norm, the mixed $l_{2,1}$ ...
On near(est) correlation matrix
Correlation matrix positive semidefinite matrix matrix nearness problem versal deformations of matrices
2013/4/27
We present an elementary heuristic reasoning based on Arnold's theory of versal deformations in support of a straightforward algorithm for finding a correlation matrix near the given symmetric one.
Matrix completion via max-norm constrained optimization
Compressed sensing low-rank matrix matrix completion max-norm con-strained minimization optimal rate of convergence sparsity
2013/4/28
This paper studies matrix completion under a general sampling model using the max-norm as a convex relaxation for the rank of the matrix. The optimal rate of convergence is established for the Frobeni...
High Dimensional Covariance Matrix Estimation in Approximate Factor Models
sparse estimation thresholding cross-sectional correlation common factors idiosyncratic seemingly unrelated regression
2011/6/20
The variance covariance matrix plays a central role in the inferential theories
of high dimensional factor models in finance and economics. Popular
regularization methods of directly exploiting spar...
All-at-once Optimization for Coupled Matrix and Tensor Factorizations
data fusion matrix factorizations tensor factorizations CANDECOMP PARAFAC missing data
2011/6/21
Joint analysis of data from multiple sources has the potential
to improve our understanding of the underlying structures
in complex data sets. For instance, in restaurant recommendation
systems, re...
Geometric sensitivity of random matrix results: consequences for shrinkage estimators of covariance and related statistical methods
random matrix related statistical shrinkage estimators
2011/6/16
Shrinkage estimators of covariance are an important tool in modern applied and theoretical statistics.
They play a key role in regularized estimation problems, such as ridge regression (aka Tykhonov
...
A Random Matrix--Theoretic Approach to Handling Singular Covariance Estimates
Random Matrix--Theoretic Approach Handling Singular Covariance Estimates
2010/10/19
In many practical situations we would like to estimate the covariance matrix of a set of variables from an insufficient amount of data. More specifically, if we have a set of $N$ independent, identica...