Penalized empirical likelihood for high-dimensionalgeneralized linear models with longitudinal data
报告人:陈夏教授,陕西师范大学 时间:2019年11月8号,10:30-11:30
摘要:In this talk, we consider the application of penalized empirical likelihood to the
high-dimensional generalized linear models with longitudinal data. Under regular conditions,it is shown that the penalized empirical likelihood has the oracle property. That is, thepenalized empirical likelihood estimators correctly select covariates with nonzero coefficientswith probability converging to one and that the estimators of nonzero coefficients have thesame asymptotic distribution that they would have if zero coefficients were known in advance.Also, we find the asymptotic distribution of the penalized empirical likelihood ratio teststatistic is the chi-square distribution. Thus the confidence regions can be constructed.Some simulations and a real data analysis are conducted to illustrate the proposed method.