The following plot shows the average estimates for 100 regressors (represented along the horizontal axis), and repeated for the OLS, Lasso and Adaptive Lasso estimators. The red dots are the estimates for the true non-zero coefficients. The black dots are the estimates of true-zero coefficients. OLS Lasso Adaptive Lasso 1.2- 12- 1.2 0.8- 08- 0.8- 04- 04- 04- 0.0-. 0- 0.0-. 100 You can learn from the plot that O a. All of the options O b. The Adaptive Lasso has better model-selection properties because it estimates more frequently the true zeros as zeros O. The Lasso has a clear shrinkage towards zero O d. The OLS has poor predictive performance e. The Adaptive Lasso has better model-selection properties because it estimates more frequently the true non-zeros as non-zeros
The following plot shows the average estimates for 100 regressors (represented along the horizontal axis), and repeated for the OLS, Lasso and Adaptive Lasso estimators. The red dots are the estimates for the true non-zero coefficients. The black dots are the estimates of true-zero coefficients. OLS Lasso Adaptive Lasso 1.2- 12- 1.2 0.8- 08- 0.8- 04- 04- 04- 0.0-. 0- 0.0-. 100 You can learn from the plot that O a. All of the options O b. The Adaptive Lasso has better model-selection properties because it estimates more frequently the true zeros as zeros O. The Lasso has a clear shrinkage towards zero O d. The OLS has poor predictive performance e. The Adaptive Lasso has better model-selection properties because it estimates more frequently the true non-zeros as non-zeros
Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter7: Distance And Approximation
Section7.3: Least Squares Approximation
Problem 31EQ
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