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pro vyhledávání: '"Arashi M"'
Directed Acyclic Graphs (DAGs) are solid structures used to describe and infer the dependencies among variables in multivariate scenarios. Having a thorough comprehension of the accurate DAG-generating model is crucial for causal discovery and estima
Externí odkaz:
http://arxiv.org/abs/2403.17489
This article explores the estimation of precision matrices in high-dimensional Gaussian graphical models. We address the challenge of improving the accuracy of maximum likelihood-based precision estimation through penalization. Specifically, we consi
Externí odkaz:
http://arxiv.org/abs/2312.15781
Modelling noisy data in a network context remains an unavoidable obstacle; fortunately, random matrix theory may comprehensively describe network environments effectively. Thus it necessitates the probabilistic characterisation of these networks (and
Externí odkaz:
http://arxiv.org/abs/2312.00728
Differential Networks (DNs), tools that encapsulate interactions within intricate systems, are brought under the Bayesian lens in this research. A novel na{\i}ve Bayesian adaptive graphical elastic net (BAE) prior is introduced to estimate the compon
Externí odkaz:
http://arxiv.org/abs/2306.14199
Bayesian methodologies prioritising accurate associations above sparsity in Gaussian graphical model (GGM) estimation remain relatively scarce in scientific literature. It is well accepted that the $\ell_2$ penalty enjoys a smaller computational foot
Externí odkaz:
http://arxiv.org/abs/2210.16290
There exist several endeavors proposing a new family of extended distributions using the beta-generating technique. This is a well-known mechanism in developing flexible distributions, by embedding the cumulative distribution function (cdf) of a base
Externí odkaz:
http://arxiv.org/abs/1912.07560
Autor:
Taavoni, M., Arashi, M.
This paper considers the problem of estimation in the generalized semiparametric model for longitudinal data when the number of parameters diverges with the sample size. A penalization type of generalized estimating equation method is proposed, while
Externí odkaz:
http://arxiv.org/abs/1909.08498
The fuzzy linear regression (FLR) modeling was first proposed making use of linear programming and then followed by many improvements in a variety of ways. In almost all approaches changing the meters, objective function, and restrictions caused to i
Externí odkaz:
http://arxiv.org/abs/1903.00351
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We propose a rescaled LASSO, by premultipying the LASSO with a matrix term, namely linear unified LASSO (LLASSO) for multicollinear situations. Our numerical study has shown that the LLASSO is comparable with other sparse modeling techniques and ofte
Externí odkaz:
http://arxiv.org/abs/1710.04795