Search results for "Variable selection"

Geographic mosaic of selection by avian predators on hindwing warning colour in a polymorphic aposematic moth

2020

AbstractWarning signals are predicted to develop signal monomorphism via positive frequency-dependent selection (+FDS) albeit many aposematic systems exhibit signal polymorphism. To understand this mismatch, we conducted a large-scale predation experiment in four locations, among which the frequencies of hindwing warning coloration of aposematic Arctia plantaginis differ. Here we show that selection by avian predators on warning colour is predicted by local morph frequency and predator community composition. We found +FDS to be strongest in monomorphic Scotland, and in contrast, lowest in polymorphic Finland, where different predators favour different male morphs. +FDS was also found in Geo…

A graphical model selection tool for mixed models

2017

Model selection can be defined as the task of estimating the performance of different models in order to choose the most parsimonious one, among a potentially very large set of candidate statistical models. We propose a graphical representation to be considered as an extension to the class of mixed models of the deviance plot proposed in the literature within the framework of classical and generalized linear models. This graphical representation allows, once a reduced number of models have been selected, to identify important covariates focusing only on the fixed effects component, assuming the random part properly specified. Nevertheless, we suggest also a standalone figure representing th…

Differential geometric LARS via cyclic coordinate descent method

2012

We address the problem of how to compute the coefficient path implicitly defined by the differential geometric LARS (dgLARS) method in a high-dimensional setting. Although the geometrical theory developed to define the dgLARS method does not need of the definition of a penalty function, we show that it is possible to develop a cyclic coordinate descent algorithm to compute the solution curve in a high-dimensional setting. Simulation studies show that the proposed algorithm is significantly faster than the prediction-corrector algorithm originally developed to compute the dgLARS solution curve.

Variable selection in the analysis of energy consumption-growth nexus

2015

There is abundant empirical literature that focuses on whether energy consumption is a critical driver of economic growth. The evolution of this literature has largely consisted of attempts to solve the problems and answer the criticisms arising from earlier studies. One of the most common criticisms is that previous work concentrates on the bivariate relationship, energy consumption–economic growth. Many authors try to overcome this critique using control variables. However, the choice of these variables has been ad hoc, made according to the subjective economic rationale of the authors. Our contribution to this literature is to apply a robust probabilistic model to select the explanatory …

Scad-elastic net and the estimation of individual tourism expenditure determinants

2014

This paper introduces the use of scad-elastic net in the assessment of the determinants of individual tourist spending. This technique approaches two main estimation-related issues of primary importance. So far studies of tourism literature have made a wide use of classic regressions, whose results might be affected by multicollinearity. In addition, because of the absence of robust economic theory on tourism behavior, regressor selection is often left to researcher's choice when not driven by non-optimal automatic criteria. Scad-elastic net is an OLS model that accounts for both these problems by including two types of parameters constraints, namely the smoothly clipped absolute deviation …

Using the dglars Package to Estimate a Sparse Generalized Linear Model

2015

dglars is a publicly available R package that implements the method proposed in Augugliaro et al. (J. R. Statist. Soc. B 75(3), 471-498, 2013) developed to study the sparse structure of a generalized linear model (GLM). This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method. The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve. dglars is a publicly available R package that implements the method proposed in Augugliaro et al. (J. R. Statist. Soc. B 75(3), 471-498, 2013) developed to study the sparse structure of a generalized linear model (GLM). This method, call…

Applying differential geometric LARS algorithm to ultra-high dimensional feature space

2009

Variable selection is fundamental in high-dimensional statistical modeling. Many techniques to select relevant variables in generalized linear models are based on a penalized likelihood approach. In a recent paper, Fan and Lv (2008) proposed a sure independent screening (SIS) method to select relevant variables in a linear regression model defined on a ultrahigh dimensional feature space. Aim of this paper is to define a generalization of the SIS method for generalized linear models based on a differential geometric approach.

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…

Using differential LARS algorithm to study the expression profile of a sample of patients with latex-fruit syndrome

2010

Natural rubber latex IgE-mediated hypersensitivity is one of the most important health problems in allergy during recent years. The prevalence of individuals allergic to latex shows an associated hypersensitivity to some plant-derived foods, especially freshly consumed fruit. This association of latex allergy and allergy to plant-derived foods is called latex-fruit syndrome. The aim of this study is to use the differential geometric generalization of the LARS algorithm to identify candidate genes that may be associated with the pathogenesis of allergy to latex or vegetable food.

A new tuning parameter selector in lasso regression

2019

Penalized regression models are popularly used in high-dimensional data analysis to carry out variable selction and model fitting simultaneously. Whereas success has been widely reported in literature, their performance largely depend on the tuning parameter that balances the trade-off between model fitting and sparsity. In this work we introduce a new tuning parameter selction criterion based on the maximization of the signal-to-noise ratio. To prove its effectiveness we applied it to a real data on prostate cancer disease.