Search results for "LASSO regression"

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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…

High-dimensional dataQuantile regression coefficients modelingTuning parameter selectionGenomic dataLasso regressionLasso regression; High-dimensional data; Genomic data; Tuning parameter selection; Quantile regression coefficients modeling; Curves clustering;Settore SECS-S/01 - StatisticaCurves clustering
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Induced smoothing in LASSO regression

The thesis is being carried out with the National research Council at the Institute of Biomedicine and Molecular Immunology "Alberto Monroy" of Palermo, where I am a fellow, under the supervision of MD Stefania La Grutta. Our research unit is focused on clinical research in allergic respiratory problems in children. In particular, we are interested in to assess the determinants of impaired lung function in a sample of outpatient asthmatic children aged between 5 and 17 years enrolled from 2011 to 2017. Our dataset is composed by n = 529 children and several covariates regarding host and environmental factors. This thesis focuses on hypothesis testing in lasso regression, when one is interes…

LASSO regression; Induced smoothing; Sandwich formula; Sparse models; Variable selection.Sparse modelVariable selection.Induced smoothingSandwich formulaSettore SECS-S/01 - StatisticaLASSO regression
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Information Dynamics Analysis: A new approach based on Sparse Identification of Linear Parametric Models*

2020

The framework of information dynamics allows to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of a complex network. The information transfer from one process to another can be quantified through Transfer Entropy, and under the assumption of joint Gaussian variables it is strictly related to the concept of Granger Causality (GC). According to the most recent developments in the field, the computation of GC entails representing the processes through a Vector Autoregressive (VAR) model and a state space (SS) model typically identified by means of the Ordinary Least Squares (OLS). In this work, we propose a new identification …

Multivariate statisticsComputer scienceEntropyGaussian0206 medical engineeringNormal Distribution02 engineering and technology01 natural sciencesLASSO regression010305 fluids & plasmassymbols.namesakeinformation TransferState Space modelsGranger causalityLasso (statistics)0103 physical sciencesStatistics::MethodologyState spaceLeast-Squares AnalysisShrinkageSparse matrixElectroencephalography020601 biomedical engineeringinformation Transfer; LASSO regression; State Space models; Granger causalityAutoregressive modelstate space modelParametric modelOrdinary least squaresLinear ModelssymbolsGranger causalityTransfer entropyAlgorithmInformation dyancamic analysi
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