Using the penalized quasi-likelihood method, the adaptive Lasso quasi-likelihood estimators in high-dimensional generalized linear model are discussed. The proposed method can perform variable selection and estimation simultaneously. Under regularity conditions, the consistency and Oracle property of the adaptive Lasso estimator are obtained. These results are examined by several simulation studies and a real data example.

generalized linear model; penalized quasi-likelihood; variable selection; Oracle property