105 citations · 110 across the 6 of their papers we have counts for
6 papers
Upper expectation parametric regression
Lu Lin, Ping Dong, Yunquan Song +1
Every observation may follow a distribution that is randomly selected in a class of distributions. It is called the distribution uncertainty. This is a fact acknowledged in some re…
Inference for biased models: a quasi-instrumental variable approach
Lu Lin, Lixing Zhu, Yujie Gai
For linear regression models who are not exactly sparse in the sense that the coefficients of the insignificant variables are not exactly zero, the working models obtained by a var…
Asymptotic Composite Estimation
Lu Lin, Feng Li, Kangning Wang +1
Composition methodologies in the current literature are mainly to promote estimation efficiency via direct composition, either, of initial estimators or of objective functions. In…
Estimation and inference for high-dimensional non-sparse models
Lu Lin, Lixing Zhu, Yujie Gai
To successfully work on variable selection, sparse model structure has become a basic assumption for all existing methods. However, this assumption is questionable as it is hard to…
Adaptive post-Dantzig estimation and prediction for non-sparse "large and small " models
Lu Lin, Lixing Zhu, Yujie Gai
For consistency (even oracle properties) of estimation and model prediction, almost all existing methods of variable/feature selection critically depend on sparsity of models. Howe…
Covariate-adjusted nonlinear regression
Xia Cui, Wensheng Guo, Lu Lin +1
In this paper, we propose a covariate-adjusted nonlinear regression model. In this model, both the response and predictors can only be observed after being distorted by some multip…