Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Armagan Artin"'
Publikováno v:
BMC Bioinformatics, Vol 11, Iss 1, p 72 (2010)
Abstract Background Tag-based techniques, such as SAGE, are commonly used to sample the mRNA pool of an organism's transcriptome. Incomplete digestion during the tag formation process may allow for multiple tags to be generated from a given mRNA tran
Externí odkaz:
https://doaj.org/article/0395c91de9fd43069b3f84629be27bd6
We study the behavior of the posterior distribution in high-dimensional Bayesian Gaussian linear regression models having $p\gg n$, with $p$ the number of predictors and $n$ the sample size. Our focus is on obtaining quantitative finite sample bounds
Externí odkaz:
http://arxiv.org/abs/1207.4854
In recent years, a rich variety of regularization procedures have been proposed for high dimensional regression problems. However, tuning parameter choice and computational efficiency in ultra-high dimensional problems remain vexing issues. The routi
Externí odkaz:
http://arxiv.org/abs/1201.3528
In recent years, a rich variety of shrinkage priors have been proposed that have great promise in addressing massive regression problems. In general, these new priors can be expressed as scale mixtures of normals, but have more complex forms and bett
Externí odkaz:
http://arxiv.org/abs/1107.4976
Publikováno v:
Biometrika, vol. 100, no. 4, pp. 1011-1018, Dec. 2013
We investigate the asymptotic behavior of posterior distributions of regression coefficients in high-dimensional linear models as the number of dimensions grows with the number of observations. We show that the posterior distribution concentrates in
Externí odkaz:
http://arxiv.org/abs/1104.4135
Publikováno v:
Statistica Sinica 23 (2013), 119-143
We propose a generalized double Pareto prior for Bayesian shrinkage estimation and inferences in linear models. The prior can be obtained via a scale mixture of Laplace or normal distributions, forming a bridge between the Laplace and Normal-Jeffreys
Externí odkaz:
http://arxiv.org/abs/1104.0861
Autor:
Armagan, Artin, Zaretzki, Russell
We introduce a new shrinkage variable selection operator for linear models which we term the \emph{adaptive ridge selector} (ARiS). This approach is inspired by the \emph{relevance vector machine} (RVM), which uses a Bayesian hierarchical linear setu
Externí odkaz:
http://arxiv.org/abs/0803.2173
This paper explores Bayesian inference for a biased sampling model in situations where the population of interest cannot be sampled directly, but rather through an indirect and inherently biased method. Observations are viewed as being the result of
Externí odkaz:
http://arxiv.org/abs/0711.3765
Autor:
Armagan, Artin, Zaretzki, Russell L.
Withdrawn due to extensions and submission as another paper.
Externí odkaz:
http://arxiv.org/abs/0711.3657
Publikováno v:
Statistica Sinica, 2013 Jan 01. 23(1), 119-143.
Externí odkaz:
https://www.jstor.org/stable/24310517