Sparse inverse covariance estimation with the graphical lasso. We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for t... Jerome,Friedman,Trevor,... - 《Biostatistics》 被引量: 3740发表: 2008年 ...
The covariance matrix of the gametic effects has an inverse that is sparse and can be constructed rapidly by a simple algorithm, provided that all individuals have marker data, but not otherwise. An equivalent model, in which the joint distribution of QTL breeding values and marker genotypes is...
inverse covariance estimation along a lambda (sparsity) path stability selection using the StARS criterion evaluate performance Obviously, for real data, skip 1-4. data(amgut1.filt) depths <- rowSums(amgut1.filt) amgut1.filt.n <- t(apply(amgut1.filt, 1, norm_to_total)) amgut1.filt....
Sparse InversE Covariance estimation for Ecological Association and Statistical Inference - githubwangsibo/SpiecEasi
Sparse inverse covariance estimation with the graphical lasso. We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for t... Jerome,Friedman,Trevor,... - 《Biostatistics》 被引量: 3742发表: 2008年 ...
The sparse inverse covariance estimation problem is commonly solved using an 1-regularized Gaus- sian maximum likelihood estimator known as "graphical lasso", but its computational cost becomes prohibitive for large data sets. A recent line of results showed-under mild assumptions-that the graph- ica...
Such models can be robustly inferred by solving the sparse inverse covariance selection (SICS) problem. With the high dimensionality of genomics data, fast methods capable of solving large instances of SICS are needed. We developed a novel network modeling tool, Ultranet, that solves the SICS ...
Sparse InversE Covariance estimation for Ecological Association and Statistical Inference - zdk123/SpiecEasi