Model selection for penalized Gaussian Graphical Models

2013

High-dimensional data refers to the case in which the number of parameters is of one or more order greater than the sample size. Penalized Gaussian graphical models can be used to estimate the conditional independence graph in high-dimensional setting. In this setting, the crucial issue is to select the tuning parameter which regulates the sparsity of the graph. In this paper, we focus on estimating the "best" tuning parameter. We propose to select this tuning parameter by minimizing an information criterion based on the generalized information criterion and to use a stability selection approach in order to obtain a more stable graph. The performance of our method is compared with the state…

Gene Expression Profile of Chronic Myeloid Leukemia Innately Resistant to Imatinib

2007

Background. Most chronic myeloid leukemia patients who receive imatinib as first line-terapy will obtain, after 12 months treatment, complete cytogenetic and molecular response . However several cases will not achieve molecular response, but their innate mechanism(s) of resistance remain poorly understood. We tried to explore the molecular events involved in innate resistance in CML. Study design. Five patients who were molecular “non responder” and seven “major” responder were investigated by using the expression profile of a set of 380 genes. Multiple testing procedure (MTP), Significance Analysis of Microarrays (SAM), Empirical Bayes Analysis of Microarrays (EBAM), False Discovery Rate (…

Cyclic coordinate for penalized Gaussian graphical models with symmetry restriction

2014

In this paper we propose two efficient cyclic coordinate algorithms to estimate structured concentration matrix in penalized Gaussian graphical models. Symmetry restrictions on the concentration matrix are particularly useful to reduce the number of parameters to be estimated and to create specific structured graphs. The penalized Gaussian graphical models are suitable for high-dimensional data.

Marketing Strategies the Agrifood Products

2020

Agri-food production represents the specific character of an area. Food and wine culture derives from this production and is a distinctive element and an important asset for the area, with important repercussions in other sectors of the economy: industry, commerce, tourism, research. Agri-food heritage is constantly evolving, and there are frequent cases of imitation products sold in non-European markets. To combat this phenomenon, the European Union has sought to reduce this risk by granting quality marks to products that demonstrate specific links to their places of origin. The objective of this work is to analyze the motivations, benefits, and costs associated with the recognition of a l…

Company Competitiveness as a Variable Success Strategy for the Territory and the Environment

2021

Supporting the fortunes of the Made in Italy agri-food brand are the best quality products which can be found throughout the Italian territory and which, in many cases, derive from Southern Italy, with a particularly interesting role assumed by Campania and Sicily.The Sicily Region boasts a varied and peculiar food and wine heritage, especially from a qualitative point of view, the Born in Sicily brand, made with excellent raw materials which constitute and support the development of important production lines within the agro-industrial sector.Among the most important food industries in Italy and on the island, there are some operating in the confectionery industry. Today the fast-growing c…

Analisi del fenomeno infortunistico nei lavoratori extracomunitari in Italia

2004

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.