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Best subset selection is an important problem in regression analysis,which has many applications in computer science and medicine. However, the existing best subset selection methods have some limitat...
中山大学岭南学院高级计量经济学课件(II:Panel Data)CH3 Covariance Structure and Robust Covariance Estimation
昆明理工大学理学院概率论与数理统计课件Chapter 4 The Expectation and Variance--Covariance&Correlation.
Sparse inverse covariance estimation with the lasso     Sparse inverse  covariance estimation  the lasso       font style='font-size:12px;'> 2015/8/21
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm— the ...
We consider the sparse inverse covariance regularization problem or graphical lasso with regularization parameter λ. Suppose the sample covariance graph formed by thresholding the entries of the sampl...
Position uncertainty is one of the most important quantities of an unorganised three-dimensional point clouds since it provides the confidence level of any parametric estimation such as surface...
For a class of linear systems with unknown parameters that lie in intervals, we present a branch and bound algorithm for computing the worst-case covariance of the state.
Least-squares covariance matrix adjustment     Symmetric matrices  linear equations  inequalities  the original matrix       font style='font-size:12px;'> 2015/8/10
We consider the problem of finding the smallest adjustment to a given symmetric n by n matrix, as measured by the Euclidean or Frobenius norm, so that it satisfies some given linear equalities and ine...
Sparse Covariance Estimation When Variables are Ordered     Sparse Covariance Estimation  Variables  Ordered       font style='font-size:12px;'> 2015/3/20
Sparse Covariance Estimation When Variables are Ordered.
Ly alpha flux power spectrum and its covariance     numerical  absorption lines       font style='font-size:12px;'> 2014/12/24
We analyse the flux power spectrum and its covariance using simulated Lyα forests. We find that pseudo-hydro techniques are good approximations of hydrodynamical simulations at high redshift. However,...
This study describes how management of grasslands is included in the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) process-based ecosystem model designed for largescale application...
In this article we study the influence of different characteristics of our assimilation system on surface ozone analyses over Europe. Emphasis is placed on the evaluation of the background error covar...
Chemical data assimilation attempts to optimally use noisy observations along with imperfect model predictions to produce a better estimate of the chemical state of the atmosphere. It is widely accept...
Wind direction plays an important role in the spread of pollutant levels over a geographical region. We discuss how to include wind directional information in the covariance function of spatial models...
In many statistical signal processing applications, the estimation of nuisance parameters and parameters of interest is strongly linked to the resulting performance. Generally, these applications deal...

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