Search results for "Lasso"

Penalized regression and clustering in high-dimensional data

The main goal of this Thesis is to describe numerous statistical techniques that deal with high-dimensional genomic data. The Thesis begins with a review of the literature on penalized regression models, with particular attention to least absolute shrinkage and selection operator (LASSO) or L1-penalty methods. L1 logistic/multinomial regression models are used for variable selection and discriminant analysis with a binary/categorical response variable. The Thesis discusses and compares several methods that are commonly utilized in genetics, and introduces new strategies to select markers according to their informative content and to discriminate clusters by offering reduced panels for popul…

Rilassometria in fase solida di tessuti di una solanacea affine a melanzana a differenti frequenze di Larmor protoniche

2008

INDAGINI SPETTROSCOPICHE SU PRODOTTI LATTIERO-CASEARI: UN APPROCCIO CHEMIOMETRICO ALLA TRACCIABILITÀ

Entre l’État et la chefferie simple : le complexe aristocratique de Vix/le mont Lassois

2021

International audience

Lasso Technique for Retrieval of a Dislocated and Impacted Esophageal Stent

2004

Colpoisterectomia con colpocleisi per prolasso utero-vaginale completo nelle donne di età >65 anni

2007

Obiettivo: riportare l’outcome obiettivo e soggettivo a lungo termine dopo colpoisterectomia con colpocleisi nelle donne avanti negli anni. Materiali e metodo: abbiamo condotto uno studio retrospettivo su 32 pazienti di età > 65 anni. Le caratteristiche delle pazienti, i dati dell’intervento e l’outcome obiettivo sono stati ottenuti dalla consultazione ambulatoriale delle cartelle cliniche delle pazienti. I dati soggettivi sono stati ottenuti mediante intervista telefonica standardizzata. Risultati: 32 pazienti di età compresa tra i 65 e gli 85 anni con prolasso utero-vaginale di 3° grado, durante un tempo di 6 anni, sono state trattate con colpoisterectomia, colpocleisi, duplicatura sub-ur…

Food resource partitioning between two sympatric temperate wrasses

2017

The present study analysed two sympatric wrasses, Thalassoma pavo and Coris julis, with similar sizes and morphologies, that are widespread in the reef habitats of the Mediterranean and the eastern Atlantic coast. Ocean warming has induced the northward movement of T. pavo, whereas C. julis has been moving to deeper habitats. In addition, under conditions of high slope of the sea bottom, T. pavo occupies shallow habitats and C. julis is in greater abundance in deeper habitats. By investigating stomach contents and prey availability in the benthos, we assessed whether the two wrasses exploit food resources by choosing different prey within the same habitat both under co-existence and segreg…

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.

A penalized approach to covariate selection through quantile regression coefficient models

2019

The coefficients of a quantile regression model are one-to-one functions of the order of the quantile. In standard quantile regression (QR), different quantiles are estimated one at a time. Another possibility is to model the coefficient functions parametrically, an approach that is referred to as quantile regression coefficients modeling (QRCM). Compared with standard QR, the QRCM approach facilitates estimation, inference and interpretation of the results, and generates more efficient estimators. We designed a penalized method that can address the selection of covariates in this particular modelling framework. Unlike standard penalized quantile regression estimators, in which model selec…