Assessment of the Results of VLBI Intra-Technique Combination Using Regularization Methods

2009 ◽  
pp. 45-51
Author(s):  
E. Tanir ◽  
R. Heinkelmann ◽  
H. Schuh ◽  
J. Kusche ◽  
J.P. van Loon
Keyword(s):  
Regularization Methods

Comparisons of regularization methods and regularization parameter selection methods in sound source identification using inverse boundary element method

2019 ◽  
Vol 67 (3) ◽  
pp. 219-227
Author(s):  
Youhong Xiao ◽  
Qingqing Song ◽  
Shaowei Li ◽  
Guoxue Lv ◽  
Zhenlin Ji
Keyword(s):  
Boundary Element Method ◽  
Boundary Element ◽  
Transfer Matrix ◽  
Sound Source ◽  
Source Identification ◽  
Regularization Parameter ◽  
Vibration Velocity ◽  
Regularization Methods ◽  
Inverse Boundary ◽  
Element Method

In noise source identification based on the inverse boundary element method (IBEM), the boundary vibration velocity is predicted based on the field pressure through a transfer matrix of the vibration velocity and field pressure established on the Helmholtz integral equation. Because the matrix is often ill-posed, it needs to be regularized before reconstructing the vibration velocity. Two regularization methods and two methods of selecting the regularization parameter are investigated through the simulation analysis of a pulsating sphere. The result of transfer matrix regularization is further verified through the reconstruction of the vibration of an aluminum plate. Additionally, to reduce the large errors at some frequencies in the reconstruction result, increasing the number of measuring points is more effective than reducing the distance between the measurement plane and the sound source.

Regularization methods for inverse problems in x-ray tomography

2010 ◽  
Author(s):  
Valeriy Titarenko ◽  
Robert Bradley ◽  
Christopher Martin ◽  
Philip J. Withers ◽  
Sofya Titarenko
Keyword(s):  
Inverse Problems ◽  
Regularization Methods ◽  
X Ray

scholarly journals Variable selection for causal mediation analysis using LASSO-based methods

2021 ◽  
pp. 096228022199750
Author(s):  
Zhaoxin Ye ◽  
Yeying Zhu ◽  
Donna L Coffman
Keyword(s):  
Propensity Score ◽  
Indirect Effect ◽  
Adaptive Lasso ◽  
Mediation Effect ◽  
Regularization Methods ◽  
Causal Mediation ◽  
Outcome Information ◽  
Causal Mediation Analysis ◽  
Selection For ◽  
Mimic Iii

Causal mediation effect estimates can be obtained from marginal structural models using inverse probability weighting with appropriate weights. In order to compute weights, treatment and mediator propensity score models need to be fitted first. If the covariates are high-dimensional, parsimonious propensity score models can be developed by regularization methods including LASSO and its variants. Furthermore, in a mediation setup, more efficient direct or indirect effect estimators can be obtained by using outcome-adaptive LASSO to select variables for propensity score models by incorporating the outcome information. A simulation study is conducted to assess how different regularization methods can affect the performance of estimated natural direct and indirect effect odds ratios. Our simulation results show that regularizing propensity score models by outcome-adaptive LASSO can improve the efficiency of the natural effect estimators and by optimizing balance in the covariates, bias can be reduced in most cases. The regularization methods are then applied to MIMIC-III database, an ICU database developed by MIT.

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