Variable Selection for Linear Regression Models with Missing Data
Peixin Zhao · Journal of Hechi University · 2009
Based on penalized estimating equations,a variable selection procedure for linear regression models is proposed.By using local quadratic approximation,an iterative algorithm is also introduced.The optimal convergence rate and the consistency are derived.A simulation study is undertaken to assess the finite sample performance of the proposed method.