Robust variable selection for nonlinear models with diverging number of parameters

We focus on the problem of simultaneous variable selection and estimation for nonlinear models based on modal regression (MR), when the number of coefficients diverges with sample size. With appropriate selection of the tuning parameters, the resulting estimator is shown to be consistent and to enjoy the oracle properties

A flexible mandatory access control policy for XML databases

A flexible mandatory access control policy (MAC) for XML databases is presented in this paper. The label type and label access policy can be defined according to the requirements of applications. In order to preserve the integrity of data in XML databases, a constraint between a read access rule and a write access rule in label access policy is introduced. Rules for label assignment and propagation are proposed to alleviate the workload of label assignment. Also, a solution for resolving conflicts of label assignments is proposed. At last, operations for implementation of the MAC policy in a XML database are illustrated

A practical mandatory access control model for XML databases

A practical mandatory access control (MAC) model for XML databases is presented in this paper. The label type and label access policy can be defined according to the requirements of different applications. In order to preserve the integrity of data in XML databases, a constraint between a read-access rule and a write-access rule in label access policy is introduced. Rules for label assignment and propagation are presented to alleviate the workload of label assignments. Furthermore, a solution for resolving conflicts in label assignments is proposed. Rules for update-related operations, rules for exceptional privileges of ordinary users and the administrator are also proposed to preserve the security of operations in XML databases. The MAC model, we proposed in this study, has been implemented in an XML database. Test results demonstrated that our approach provides rational and scalable performance

Robust variable selection in partially varying coefficient single-index model

By combining basis function approximations and smoothly clipped absolute deviation (SCAD) penalty, this paper proposes a robust variable selection procedure for a partially varying coefficient single-index model based on modal regression. The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components. With appropriate selection of the tuning parameters, we establish the theoretical properties of our procedure, including consistency in variable selection and the oracle property in estimation. Furthermore, we also discuss the bandwidth selection and propose a modified expectation-maximization (EM)-type algorithm for the proposed estimation procedure. The finite sample properties of the proposed estimators are illustrated by some simulation examples.The research of Zhu is partially supported by National Natural Science Foundation of China (NNSFC) under Grants 71171075, 71221001 and 71031004. The research of Yu is supported by NNSFC under Grant 11261048