Results 31 to 40 of about 245,585 (258)
Predicting potential biomarkers and immune infiltration characteristics in heart failure
Mathematical Biosciences and Engineering, 2022 Background: Studies have demonstrated that immune cell activation and their infiltration in the myocardium can have adverse effects on the heart, contributing to the pathogenesis of heart failure (HF).
Xuesi Chen , Qijun Zhang, Qin Zhang
doaj +1 more source
Frontiers in Genetics, 2021 Motivation: The Cox proportional hazard models are widely used in the study of cancer survival. However, these models often meet challenges such as the large number of features and small sample sizes of cancer data sets. While this issue can be partially
Gabriela Malenová +4 more
doaj +1 more source
Pivotal estimation via square-root Lasso in nonparametric regression [PDF]
, 2013 We propose a self-tuning $\sqrt{\mathrm {Lasso}}$ method that simultaneously resolves three important practical problems in high-dimensional regression analysis, namely it handles the unknown scale, heteroscedasticity and (drastic) non-Gaussianity of the
Belloni, Alexandre +2 more
core +3 more sources
Annals of the Institute of Statistical Mathematics, 2013 We propose the Bayesian adaptive Lasso (BaLasso) for variable selection and coefficient estimation in linear regression. The BaLasso is adaptive to the signal level by adopting different shrinkage for different coefficients. Furthermore, we provide a model selection machinery for the BaLasso by assessing the posterior conditional mode estimates ...
Leng, C., Tran, M.-N., Nott, D.
openaire +2 more sources
Cognitive Computation, 2016 Lasso-type variable selection has been demonstrated to be effective in handling high-dimensional data. From the biological perspective, traditional Lasso-type models are capable of learning which stimuli are valuable while ignoring the many that are not, and thus perform feature selection.
Zhihong Zhang +6 more
openaire +3 more sources
lassopack: Model selection and prediction with regularized regression in Stata [PDF]
, 2019 This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS.
Athey S. +6 more
core +2 more sources
Distinguished Minimal Topological Lassos [PDF]
SIAM Journal on Discrete Mathematics, 2015 A classical result in distance based tree-reconstruction characterizes when for a distance $D$ on some finite set $X$ there exist a uniquely determined dendrogram on $X$ (essentially a rooted tree $T=(V,E)$ with leaf set $X$ and no degree two vertices but possibly the root and an edge weighting $ :E\to \mathbb R_{\geq 0}$) such that the distance $D_ ...
Huber, Katharina T. +1 more
openaire +4 more sources
REAL-TIME RADIOACTIVE PRECURSOR OF THE APRIL 16, 2016 Mw 7.8 EARTHQUAKE AND TSUNAMI IN ECUADOR [PDF]
Science of Tsunami Hazards, 2018 On the 16th of April 2016, a Mw 7.8 earthquake with a minor tsunami impacted coastal Ecuador, being the most devastating seismic event registered in northern South America in this century so far.
Theofilos Toulkeridis +5 more
doaj
Measurement Error in Lasso: Impact and Correction
, 2014 Regression with the lasso penalty is a popular tool for performing dimension reduction when the number of covariates is large. In many applications of the lasso, like in genomics, covariates are subject to measurement error.
Frigessi, Arnoldo +2 more
core +1 more source
Computational Statistics & Data Analysis, 2007 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +1 more source