Results 11 to 20 of about 2,762 (131)

A Commentary on Chatterjee Et Al. (2018): A Corrected Framework for Group Sparsity in Zero-Inflated Negative Binomial Models. [PDF]

Stat Med
ABSTRACT We reexamine GOOOGLE, the group‐regularized zero‐inflated negative binomial (ZINB) approach of Chatterjee et al. We show that in the released implementation, the tuning parameter is selected using a Bayesian information criterion (BIC) computed on a Gaussian surrogate.
Iqbal A, Mallick H, Ogundimu EO.
europepmc   +2 more sources

Variable selection in modelling clustered data via within-cluster resampling. [PDF]

Can J Stat
Abstract In many biomedical applications, there is a need to build risk‐adjustment models based on clustered data. However, methods for variable selection that are applicable to clustered discrete data settings with a large number of candidate variables and potentially large cluster sizes are lacking.
Ye S, Yu T, Caroff DA, Huang SS, Zhang B, Wang R.   +5 more
europepmc   +2 more sources

Approximate Message Passing Algorithm for Nonconvex Regularization

IEEE Access, 2019
In this paper, we study the sparse signal reconstruction with nonconvex regularization, mainly focusing on two popular nonconvex regularization methods, minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD).
Hui Zhang, Hai Zhang, Yong Liang, Zi-Yi Yang, Yanqiong Ren   +4 more
doaj   +1 more source

Linear Convergence of Adaptively Iterative Thresholding Algorithms for Compressed Sensing [PDF]

, 2015
This paper studies the convergence of the adaptively iterative thresholding (AIT) algorithm for compressed sensing. We first introduce a generalized restricted isometry property (gRIP).
Chang, Xiangyu, Peng, Zhimin, Wang, Yu, Xu, Zongben, Zeng, Jinshan   +4 more
core   +1 more source

Estimation of Genomic Breed Composition for Purebred and Crossbred Animals Using Sparsely Regularized Admixture Models

Frontiers in Genetics, 2020
A variety of statistical methods, such as admixture models, have been used to estimate genomic breed composition (GBC). These methods, however, tend to produce non-zero components to reference breeds that shared some genomic similarity with a test animal.
Yangfan Wang, Yangfan Wang, Xiao-Lin Wu, Xiao-Lin Wu, Zhi Li, Zhi Li, Zhenmin Bao, Zhenmin Bao, Richard G. Tait, Stewart Bauck, Guilherme J. M. Rosa   +10 more
doaj   +1 more source

Sparse generalized linear model with L 0 approximation for feature selection and prediction with big omics data

BioData Mining, 2017
Background Feature selection and prediction are the most important tasks for big data mining. The common strategies for feature selection in big data mining are L 1, SCAD and MC+.
Zhenqiu Liu, Fengzhu Sun, Dermot P. McGovern   +2 more
doaj   +1 more source

SIS: An R Package for Sure Independence Screening in Ultrahigh-Dimensional Statistical Models

Journal of Statistical Software, 2018
We revisit sure independence screening procedures for variable selection in generalized linear models and the Cox proportional hazards model. Through the publicly available R package SIS, we provide a unified environment to carry out variable selection ...
Diego Franco Saldana, Yang Feng
doaj   +1 more source

On the adaptive elastic-net with a diverging number of parameters [PDF]

, 2009
We consider the problem of model selection and estimation in situations where the number of parameters diverges with the sample size. When the dimension is high, an ideal method should have the oracle property [J. Amer. Statist. Assoc.
Zhang, Hao Helen, Zou, Hui
core   +2 more sources

Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in Brachypodium distachyon

Frontiers in Plant Science, 2017
We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX) model with a group smoothly clipped absolute deviation (SCAD) method using a time ...
Satoru Koda, Yoshihiko Onda, Hidetoshi Matsui, Kotaro Takahagi, Kotaro Takahagi, Yukiko Uehara-Yamaguchi, Minami Shimizu, Komaki Inoue, Takuhiro Yoshida, Tetsuya Sakurai, Tetsuya Sakurai, Hiroshi Honda, Shinto Eguchi, Ryuei Nishii, Keiichi Mochida, Keiichi Mochida, Keiichi Mochida   +16 more
doaj   +1 more source

Approximate message passing for nonconvex sparse regularization with stability and asymptotic analysis

, 2018
We analyse a linear regression problem with nonconvex regularization called smoothly clipped absolute deviation (SCAD) under an overcomplete Gaussian basis for Gaussian random data.
Sakata, Ayaka, Xu, Yingying
core   +1 more source