Zobrazeno 61 - 70
of 326
pro vyhledávání: '"Lin, Lizhen"'
We study full Bayesian procedures for sparse linear regression when errors have a symmetric but otherwise unknown distribution. The unknown error distribution is endowed with a symmetrized Dirichlet process mixture of Gaussians. For the prior on regr
Externí odkaz:
http://arxiv.org/abs/1608.02143
Community detection, which focuses on clustering nodes or detecting communities in (mostly) a single network, is a problem of considerable practical interest and has received a great deal of attention in the research community. While being able to cl
Externí odkaz:
http://arxiv.org/abs/1606.02401
Publikováno v:
The Annals of Statistics, 2019 Dec 01. 47(6), 3413-3437.
Externí odkaz:
https://www.jstor.org/stable/26867171
Asymptotic theory of tail index estimation has been studied extensively in the frequentist literature on extreme values, but rarely in the Bayesian context. We investigate whether popular Bayesian kernel mixture models are able to support heavy taile
Externí odkaz:
http://arxiv.org/abs/1511.02775
Autor:
Lin, Lizhen
This thesis proposes some new model independent or nonparametric methods for estimating the dose-response curve and the effective dosage curve in the context of bioassay. The research problem is also of importance in environmental risk assessment and
Externí odkaz:
http://hdl.handle.net/10150/222849
We propose an extrinsic regression framework for modeling data with manifold valued responses and Euclidean predictors. Regression with manifold responses has wide applications in shape analysis, neuroscience, medical imaging and many other areas. Ou
Externí odkaz:
http://arxiv.org/abs/1508.02201
Publikováno v:
In Progress in Oceanography November-December 2020 189
Autor:
Huang, Mei, Zheng, Li, Xu, Hui, Tang, Damu, Lin, Lizhen, Zhang, Jin, Li, Cuifang, Wang, Wei, Yuan, Qiongjing, Tao, Lijian, Ye, Zunlong
Publikováno v:
In Journal of Molecular and Cellular Cardiology January 2020 138:256-268
We present a data augmentation scheme to perform Markov chain Monte Carlo inference for models where data generation involves a rejection sampling algorithm. Our idea, which seems to be missing in the literature, is a simple scheme to instantiate the
Externí odkaz:
http://arxiv.org/abs/1406.6652
We introduce a Bayesian model for inferring mixtures of subspaces of different dimensions. The key challenge in such a mixture model is specification of prior distributions over subspaces of different dimensions. We address this challenge by embeddin
Externí odkaz:
http://arxiv.org/abs/1404.6841