In this paper, we propose a lower bound based smoothed quasi-Newton algorithm for computing the solution paths of the group bridge estimator in linear regression models. Our method is based on the quasi-Newton algorithm with a smoothed group bridge penalty in combination with a novel data-driven thresholding rule for the regression coefficients. This rule is derived based on a necessary KKT condition of the group bridge optimization problem. It is easy to implement and can be used to eliminate groups with zero coefficients. Thus, it reduces the dimension of the optimization problem. The proposed algorithm removes the restriction of groupwise orthogonal condition needed in coordinate descent and LARS algorithms for group variable selection. Numerical results show that the proposed algorithm outperforms the coordinate descent based algorithms in both efficiency and accuracy. 相似文献
This study examines the effects of professionalization on the cost efficiency of fundraising organizations in a unique research context, Chinese charitable foundations. Two important professionalization measures, professionalized human resource management and accounting practices, are adopted. Using data from audited annual reports from 2005 to 2009, we find that professionalization in general enables foundations to increase their fundraising cost efficiencies. However, further analysis indicates that this positive effect only occurs in private but not in public foundations. Furthermore, the positive effect of professionalization is more significant when raising unrestricted funds than when raising restricted funds from donors.