Search results for "stability selection"
showing 2 items of 2 documents
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…
Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds
2018
The identification of individuals’ breed of origin has several practical applications in livestock and is useful in different biological contexts such as conservation genetics, breeding and authentication of animal products. In this paper, penalized multinomial regression was applied to identify the minimum number of single nucleotide polymorphisms (SNPs) from high-throughput genotyping data for individual assignment to dairy sheep breeds reared in Sicily. The combined use of penalized multinomial regression and stability selection reduced the number of SNPs required to 48. A final validation step on an independent population was carried out obtaining 100% correctly classified individuals. …