Results 51 to 60 of about 257,684 (210)
Sparsity with sign-coherent groups of variables via the cooperative-Lasso [PDF]
, 2012 We consider the problems of estimation and selection of parameters endowed with a known group structure, when the groups are assumed to be sign-coherent, that is, gathering either nonnegative, nonpositive or null parameters.
Charbonnier, Camille +2 more
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Clinical and Translational Discovery, 2023 Background In recent years, it has become a research focus to accurately extract key genes influencing the occurrence and development of diseases from massive genomic data and study their regulatory mechanisms.
Weixiao Bu +7 more
doaj +1 more source
Robust Lasso-Zero for sparse corruption and model selection with missing covariates
, 2020 We propose Robust Lasso-Zero, an extension of the Lasso-Zero methodology [Descloux and Sardy, 2018], initially introduced for sparse linear models, to the sparse corruptions problem.
Boyer, Claire +4 more
core +2 more sources
Journal of Computational and Graphical Statistics, 2019 We propose a generalization of the lasso that allows the model coefficients to vary as a function of a general set of some prespecified modifying variables. These modifiers might be variables such as gender, age, or time. The paradigm is quite general, with each lasso coefficient modified by a sparse linear function of the modifying variables Z.
Tibshirani, Robert, Friedman, Jerome
openaire +3 more sources
Frontiers in Endocrinology, 2023 BackgroundThis study aimed to develop an autoimmune thyroid disease (AITD) risk prediction model for patients with vitiligo based on readily available characteristics.MethodsA retrospective analysis was conducted on the clinical characteristics ...
Ze Ma +6 more
doaj +1 more source
Pick-and-eat space crop production flight testing on the International Space Station
Journal of Plant InteractionsFresh, nutritious, palatable produce for crew consumption on long-duration spaceflight missions may provide health-promoting, bioavailable nutrients and enhance the dietary experience.
Jess M. Bunchek +9 more
doaj +1 more source
Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
, 2016 In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance. It is customary to consider $\ell_1$ penalty to enforce sparsity in such scenarios.
Fercoq, Olivier +4 more
core +3 more sources
Revista de Relaciones Internacionales, Estrategia y SeguridadLa investigación tiene como objetivo analizar el lavado de activos como un fenómeno social contemporáneo en los panoramas internacional y regional, y describir sus efectos sociales, políticos y económicos en Ecuador.
Edison Patricio Cisneros Corrales +1 more
doaj +1 more source
Adaptive robust variable selection
, 2014 Heavy-tailed high-dimensional data are commonly encountered in various scientific fields and pose great challenges to modern statistical analysis. A natural procedure to address this problem is to use penalized quantile regression with weighted $L_1 ...
Barut, Emre +2 more
core +1 more source
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013 Negative co-occurrence is a common phenomenon in many signal processing applications. In some cases the signals involved are sparse, and this information can be exploited to recover them. In this paper, we present a sparse learning approach that explicitly takes into account negative co-occurrence. This is achieved by adding a novel penalty term to the
Luengo García, David +4 more
openaire +2 more sources