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][1], Chapter 3.

  [1]: http://www-stat.stanford.edu/~tibs/ElemStatLearn/