Skip to main content
Cross Validated

Return to Answer

deleted 7 characters in body
Source Link
User1865345
User1865345
  • 10.3k
  • 12
  • 23
  • 40

I've never heard of Kuk's method, but the hot topic these days is L1 minimisation. The rationale being that if you use a penalty term of the absolute value of the regression coefficients, the unimportant ones should go to zero.

These techniques have some funny names: Lasso, LARS, Dantzig selector. You can read the papers, but a good place to start is with Elements of Statistical LearningElements of Statistical Learning, Chapter 3.

I've never heard of Kuk's method, but the hot topic these days is L1 minimisation. The rationale being that if you use a penalty term of the absolute value of the regression coefficients, the unimportant ones should go to zero.

These techniques have some funny names: Lasso, LARS, Dantzig selector. You can read the papers, but a good place to start is with Elements of Statistical Learning, Chapter 3.

I've never heard of Kuk's method, but the hot topic these days is L1 minimisation. The rationale being that if you use a penalty term of the absolute value of the regression coefficients, the unimportant ones should go to zero.

These techniques have some funny names: Lasso, LARS, Dantzig selector. You can read the papers, but a good place to start is with Elements of Statistical Learning, Chapter 3.

Source Link
Simon Byrne
Simon Byrne
  • 3.5k
  • 20
  • 31

I've never heard of Kuk's method, but the hot topic these days is L1 minimisation. The rationale being that if you use a penalty term of the absolute value of the regression coefficients, the unimportant ones should go to zero.

These techniques have some funny names: Lasso, LARS, Dantzig selector. You can read the papers, but a good place to start is with Elements of Statistical Learning, Chapter 3.