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n the following plot you see the rejection frequencies (defined as the frequency in which the estimator is non-zero) 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 rue non-zero coefficients. The black dots are the estimates of true-zero coefficients. OLS Lasso Adaptive Lasso 08- 08- 0.8 04- 0.4- 0.4 00- 00- 0.0- "ou can learn from the plots that: O a. The Lasso has a clear shrinkage towards zero O b. The Adaptive Lasso has better model-selection properties because it estimates more frequently the true zeros as zeros Oc. All of the options O d. The OLS has poor predictive performance O e. The Adaptive Lasso has better model-selection properties because it estimates more frequently the true non-zeros as non-zeros coef

n the following plot you see the rejection frequencies (defined as the frequency in which the estimator is non-zero) 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 rue non-zero coefficients. The black dots are the estimates of true-zero coefficients. OLS Lasso Adaptive Lasso 08- 08- 0.8 04- 0.4- 0.4 00- 00- 0.0- "ou can learn from the plots that: O a. The Lasso has a clear shrinkage towards zero O b. The Adaptive Lasso has better model-selection properties because it estimates more frequently the true zeros as zeros Oc. All of the options O d. The OLS has poor predictive performance O e. The Adaptive Lasso has better model-selection properties because it estimates more frequently the true non-zeros as non-zeros coef

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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In the following plot you see the rejection frequencies (defined as the frequency in which the estimator is non-zero) 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 12- 12- 1.2- 0.8- 0.8- 0.8- 0.4- 0.4- 0.4- 0.0- 0.0- 0.0- 100 100 You can learn from the plots that: O a. The Lasso has a clear shrinkage towards zero O b. The Adaptive Lasso has better model-selection properties because it estimates more frequently the true zeros as zeros O. All of the options Od. The OLS has poor predictive performance O e. The Adaptive Lasso has better model-selection properties because it estimates more frequently the true non-zeros as non-zeros coef *8-
Transcribed Image Text:In the following plot you see the rejection frequencies (defined as the frequency in which the estimator is non-zero) 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 12- 12- 1.2- 0.8- 0.8- 0.8- 0.4- 0.4- 0.4- 0.0- 0.0- 0.0- 100 100 You can learn from the plots that: O a. The Lasso has a clear shrinkage towards zero O b. The Adaptive Lasso has better model-selection properties because it estimates more frequently the true zeros as zeros O. All of the options Od. The OLS has poor predictive performance O e. The Adaptive Lasso has better model-selection properties because it estimates more frequently the true non-zeros as non-zeros coef *8-
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