Search results for "lasso"
showing 10 items of 110 documents
Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study
2019
The EUGEI project was supported by the grant agreement HEALTH-F2-2010-241909 from the European Community’s Seventh Framework Programme. The authors are grateful to the patients and their families for participating in the project. They also thank all research personnel involved in the GROUP project, in particular J. van Baaren, E. Veermans, G. Driessen, T. Driesen, E. van’t Hag and J. de Nijs. Bart PF Rutten was funded by a VIDI award number 91718336 from the Netherlands Scientific Organisation.
ℓ1-Penalized Methods in High-Dimensional Gaussian Markov Random Fields
2016
In the last 20 years, we have witnessed the dramatic development of new data acquisition technologies allowing to collect massive amount of data with relatively low cost. is new feature leads Donoho to define the twenty-first century as the century of data. A major characteristic of this modern data set is that the number of measured variables is larger than the sample size; the word high-dimensional data analysis is referred to the statistical methods developed to make inference with this new kind of data. This chapter is devoted to the study of some of the most recent ℓ1-penalized methods proposed in the literature to make sparse inference in a Gaussian Markov random field (GMRF) defined …
Spekulaation paluu
2017
Kirja-arvio teoksesta Quentin Meillassoux, Äärellisyyden jälkeen. Tutkielma kontingenssin välttämättömyydestä (Après la finitude. Essai sur la nécessité de la contingence, 2006). Suom. Ari Korhonen. Gaudeamus, Helsinki 2017. 286 s. nonPeerReviewed
Kaaos ilman perustaa : Quentin Meillassoux’n korrelationismikritiikki, eli kuinka ajatella absoluuttia
2017
Tutkielmassa analysoin Quentin Meillassoux'n korrelationistisen filosofian kritiikkiä ja todellisuuden radikaalisti kontingentin luonteen -argumenttia.
From the Ultimate God to the Virtual God: Post-Ontotheological Perspectives on the Divine in Heidegger, Badiou, and Meillassoux
2014
The Heideggerian account of the ontotheological constitution of Western metaphysics has been extremely influential for contemporary philosophy of religion and for philosophical perspectives on theology and the divine. This paper introduces and contrasts two central strategies for approaching the question of the divine in a non- or post-ontotheological manner. The first and more established approach is that of post-Heideggerian hermeneutics and deconstruction, inspired by Heidegger’s suggestion of a “theology without the word ‘being’” and by his later notions of an “ultimate god” and of “divinities” as one of the four axes of the fourfold (Geviert). Here, the divine is no longer articulated …
Single-trial Connectivity Estimation through the Least Absolute Shrinkage and Selection Operator.
2019
Methods based on the use of multivariate autoregressive models (MVAR) have proved to be an accurate tool for the estimation of functional links between the activity originated in different brain regions. A well-established method for the parameters estimation is the Ordinary Least Square (OLS) approach, followed by an assessment procedure that can be performed by means of Asymptotic Statistic (AS). However, the performances of both procedures are strongly influenced by the number of data samples available, thus limiting the conditions in which brain connectivity can be estimated. The aim of this paper is to introduce and test a regression method based on Least Absolute Shrinkage and Selecti…
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 …
Model-Based Transfer Entropy Analysis of Brain-Body Interactions with Penalized regression techniques
2020
The human body can be seen as a functional network depicting the dynamical interactions between different organ systems. This exchange of information is often evaluated with information-theoretic approaches which comprise the use of vector autoregressive (VAR) and state space (SS) models, normally identified with the Ordinary Least Squares (OLS). However, the number of time series to be included in the model is strictly related to the length of data recorded thus limiting the use of the classical approach. In this work, a new method based on penalized regressions, the so-called LASSO, was compared with OLS on physiological time-series extracted from 18 subjects during different stress condi…
Propagation pattern analysis during atrial fibrillation based on sparse modeling.
2012
In this study, sparse modeling is introduced for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence function, derived from fitting a multivariate autoregressive model to the observed signal using least-squares (LS) estimation. The propagation pattern analysis incorporates prior information on sparse coupling as well as the distance between the recording sites. Two optimization methods are employed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO), and a novel method named the distance-adaptive group LASSO (dLASSO). Using si…
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…