Selection of the linear regression model according to the parameter estimation

2000 ◽  
Vol 5 (4) ◽  
pp. 400-405 ◽  
Author(s):  
Sun Dao-de
Keyword(s):  
Parameter Estimation ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Selection Of

scholarly journals The selective regularization of a linear regression model

2021 ◽  
Vol 2099 (1) ◽  
pp. 012024
Author(s):  
V N Lutay ◽  
N S Khusainov
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Ridge Regression ◽  
Triangular Decomposition ◽  
System Matrix ◽  
Original Dataset ◽  
The Matrix ◽  
Matrix Conditioning ◽  
Selection Of

Abstract This paper discusses constructing a linear regression model with regularization of the system matrix of normal equations. In contrast to the conventional ridge regression, where positive parameters are added to all diagonal terms of a matrix, in the method proposed only those matrix diagonal entries that correspond to the data with a high correlation are increased. This leads to a decrease in the matrix conditioning and, therefore, to a decrease in the corresponding coefficients of the regression equation. The selection of the entries to be increased is based on the triangular decomposition of the correlation matrix of the original dataset. The effectiveness of the method is tested on a known dataset, and it is performed not only with a ridge regression, but also with the results of applying the widespread algorithms LARS and Lasso.

scholarly journals Hidden Markov Linear Regression Model and its Parameter Estimation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 187037-187042
Author(s):  
Hefei Liu ◽  
Kunqjnu Wang ◽  
Yong Li
Keyword(s):  
Parameter Estimation ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Hidden Markov
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