Bayesian adaptive Lasso estimation of large graphical model based on modified Cholesky decomposition

被引：0

作者：

Li, Fanqun ^{[1
]}

Zhao, Mingtao ^{[1
]}

Zhang, Kongsheng ^{[1
]}

机构：

[1] Anhui Univ Finance & Econ, Inst Stat & Appl Math, Bengbu 233000, Peoples R China

来源：

STATISTICS & PROBABILITY LETTERS | 2024年 / 206卷

关键词：

Graphical model; Regression; Bayesian adaptive Lasso; Modified Cholesky decomposition; SELECTION; REGRESSION;

D O I：

10.1016/j.spl.2023.110004

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper, based on the modified Cholesky decomposition of the precision matrix, we propose Bayesian adaptive Lasso estimation and maximum adaptive posterior estimation for graphical model. We also recover the graph by minimizing the decoupled shrinkage and selection loss function.

引用

页数：7

共 50 条

[21] Research on Bayesian Model Averaging for Lasso based on Analysis of Scientific Materials [J].

Guo, Aotuo .

ADVANCED RESEARCH ON MATERIAL ENGINEERING, CHEMISTRY AND BIOINFORMATICS, PTS 1 AND 2 (MECB 2011), 2011, 282-283 :334-337

[22] Variational inference on a Bayesian adaptive lasso Tobit quantile regression model [J].

Wang, Zhiqiang ;

Wu, Ying ;

Cheng, WeiLi .

STAT, 2023, 12 (01)

[23] Efficient parameter estimation via modified Cholesky decomposition for quantile regression with longitudinal data [J].

Jing Lv ;

Chaohui Guo .

Computational Statistics, 2017, 32 :947-975

[24] Robust estimation via modified Cholesky decomposition for modal partially nonlinear models with longitudinal data [J].

Lu, Fei .

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2023, 52 (10) :5110-5123

[25] A LASSO BASED ENSEMBLE EMPIRICAL MODE DECOMPOSITION APPROACH TO DESIGNING ADAPTIVE CLUTTER SUPPRESSION FILTERS [J].

Shen, Zhiyuan ;

Lee, Chin-Hui .

2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2012, :757-760

[26] A Cholesky-based estimation for large-dimensional covariance matrices [J].

Kang, Xiaoning ;

Xie, Chaoping ;

Wang, Mingqiu .

JOURNAL OF APPLIED STATISTICS, 2020, 47 (06) :1017-1030

[27] Graphical Model-Based Lasso for Weakly Dependent Time Series of Tensors [J].

Ofori-Boateng, Dorcas ;

Goel, Jaidev ;

Cribben, Ivor ;

Gel, Yulia R. .

MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES: RESEARCH TRACK, PT V, ECML PKDD 2024, 2024, 14945 :249-264

[28] A Bayesian approach to paired comparison rankings based on a graphical model [J].

Kim, HJ .

COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2005, 48 (02) :269-290

[29] A Matrix-Free Posterior Ensemble Kalman Filter Implementation Based on a Modified Cholesky Decomposition [J].

Nino-Ruiz, Elias D. .

ATMOSPHERE, 2017, 8 (07)

[30] Structural and parametric uncertainties in full Bayesian and graphical lasso based approaches: beyond edge weights in psychological networks [J].

Hullam, Gabor ;

Juhasz, Gabriella ;

Deakin, Bill ;

Antal, Peter .

2017 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (CIBCB), 2017, :300-307

← 1 2 3 4 5 →