Variance component estimation for gene expression data.

<p>Estimates of random effects covariance matrices across the ten settings that had ten non-zero regression coefficients (settings 5–14, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012143#pcbi.1012143.t001" target="_blank">Table 1&l...

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Bibliographic Details
Main Author:	Evan Gorstein (20553314) (author)
Other Authors:	Rosa Aghdam (550735) (author), Claudia Solís-Lemus (2547481) (author)
Published:	2025
Subjects:	Genetics Evolutionary Biology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified two popular penalties penalized likelihood approach least absolute shrinkage increasingly important form wrap "> highdimmixedmodels penalty consistently outperforms div >< p coordinate descent algorithm wide association variable selection use =" three types selection operator real examples models relies lacks guarantees julia package global optimum fit models empower researchers empirically study effects models dimensional mixed covariates rivals algorithm ’
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Variance component estimation for gene expression data.

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