scholarly journals On guaranteed parameter estimation of a multiparameter linear regression process

Automatica ◽  
2010 ◽  
Vol 46 (4) ◽  
pp. 637-646 ◽  
Author(s):  
Uwe Küchler ◽  
Vyacheslav A. Vasiliev
Keyword(s):  
Parameter Estimation ◽  
Linear Regression

The Spike-and-Slab Lasso regression modeling with compositional covariates: An application on Brazilian children malnutrition data

2019 ◽  
Vol 29 (5) ◽  
pp. 1434-1446
Author(s):  
Francisco Louzada ◽  
Taciana KO Shimizu ◽  
Adriano K Suzuki
Keyword(s):  
Parameter Estimation ◽  
Linear Regression ◽  
Large Scale ◽  
Compositional Data ◽  
Child Malnutrition ◽  
Lasso Regression ◽  
Brazilian Child ◽  
Ilr Coordinates ◽  
Log Ratio ◽  
Brazilian Children

There are considerable challenges in analyzing large-scale compositional data. In this paper, we introduce the Spike-and-Slab Lasso linear regression in the presence of compositional covariates for parameter estimation and variable selection. We consider the well-known isometric log-ratio (ilr) coordinates to avoid misleading statistical inference. The separable and non-separable (adaptative) Spike-and-Slab Lasso penalties are compared to verify the advantages of each approach. The proposed method is illustrated on simulated and on real Brazilian child malnutrition data.

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