Search results for " LASSO"
showing 10 items of 25 documents
Entre l’État et la chefferie simple : le complexe aristocratique de Vix/le mont Lassois
2021
International audience
Propagation pattern analysis during atrial fibrillation based on the adaptive group LASSO.
2012
The present study introduces sparse modeling for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence (PDC) function, derived from fitting a multivariate autoregressive model to the observed signals. A sparse optimization method is proposed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO). In simulations aLASSO was found superior to the commonly used least-squares (LS) estimation with respect to estimation performance. The normalized error between the true and estimated model parameters dropped from 0.200.04 for LS estimatio…
An efficient algorithm to estimate the sparse group structure of an high-dimensional generalized linear model
2014
Massive regression is one of the new frontiers of computational statistics. In this paper we propose a generalization of the group least angle regression method based on the differential geometrical structure of a generalized linear model specified by a fixed and known group structure of the predictors. An efficient algorithm is also proposed to compute the proposed solution curve.
Variable selection with unbiased estimation: the CDF penalty
2022
We propose a new SCAD-type penalty in general regression models. The new penalty can be considered a competitor of the LASSO, SCAD or MCP penalties, as it guarantees sparse variable selection, i.e., null regression coefficient estimates, while attenuating bias for the non-null estimates. In this work, the method is discussed, and some comparisons are presented.
The Joint Censored Gaussian Graphical Lasso Model
2022
The Gaussian graphical model is one of the most used tools for inferring genetic networks. Nowadays, the data are often collected from different sources or under different biological conditions, resulting in heterogeneous datasets that exhibit a dependency structure that varies across groups. The complex structure of these data is typically recovered using regularized inferential procedures that use two penalties, one that encourages sparsity within each graph and the other that encourages common structures among the different groups. To this date, these approaches have not been developed for handling the case of censored data. However, these data are often generated by gene expression tech…
A differential-geometric approach to generalized linear models with grouped predictors
2016
We propose an extension of the differential-geometric least angle regression method to perform sparse group inference in a generalized linear model. An efficient algorithm is proposed to compute the solution curve. The proposed group differential-geometric least angle regression method has important properties that distinguish it from the group lasso. First, its solution curve is based on the invariance properties of a generalized linear model. Second, it adds groups of variables based on a group equiangularity condition, which is shown to be related to score statistics. An adaptive version, which includes weights based on the Kullback-Leibler divergence, improves its variable selection fea…
Vix et son territoire
2020
Do gender wage differences within households influence women's empowerment and welfare? : Evidence from Ghana
2021
Using household data from the latest wave of the Ghana Living Standards Survey, this paper utilizes machine learning techniques – IV LASSO – that allows for the treatment of unconfoundedness in the selection of observables and unobservables to examine the structural effect of gender wage differences within households on women's empowerment and welfare in Ghana. The structural parameters of the IV LASSO estimations show that a reduction in household gender wage gap significantly enhances women's empowerment. Also, a decline in household gender wage gap results meaningfully in improving household and women's welfare. Particularly, the increasing effect on women's welfare resulting from decrea…
Palermo tra innesti e piante originarie
2019
Se vi è qualcosa di profondamente e visceralmente connaturato alla stessa dimensione esistenziale della città di Palermo, sin dalle sue molteplici e stratificate origini fenicio-puniche, questo è certamente il concetto di “innesto”. Ed in analogia con l’innesto agrario, cioè con la pratica del far concrescere in una pianta esistente una parte di un altro vegetale, al fine di rafforzare il primo soggetto ma modificandolo verso un genere diverso da quello iniziale, l’intera storia millenaria della città potrà essere riguardata come il frutto di continue, cicliche introduzioni di modelli architettonici e urbani esogeni, declinati rispetto alle contingenze culturali autoctone dei diversi esempi…
L1-Penalized Censored Gaussian Graphical Model
2018
Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithm…