@article{ni_zhang_zhang_2009, title={Automatic model selection for partially linear models}, volume={100}, ISSN={["0047-259X"]}, DOI={10.1016/j.jmva.2009.06.009}, abstractNote={We propose and study a unified procedure for variable selection in partially linear models. A new type of double-penalized least squares is formulated, using the smoothing spline to estimate the nonparametric part and applying a shrinkage penalty on parametric components to achieve model parsimony. Theoretically we show that, with proper choices of the smoothing and regularization parameters, the proposed procedure can be as efficient as the oracle estimator [J. Fan, R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, Journal of American Statistical Association 96 (2001) 1348–1360]. We also study the asymptotic properties of the estimator when the number of parametric effects diverges with the sample size. Frequentist and Bayesian estimates of the covariance and confidence intervals are derived for the estimators. One great advantage of this procedure is its linear mixed model (LMM) representation, which greatly facilitates its implementation by using standard statistical software. Furthermore, the LMM framework enables one to treat the smoothing parameter as a variance component and hence conveniently estimate it together with other regression coefficients. Extensive numerical studies are conducted to demonstrate the effective performance of the proposed procedure.}, number={9}, journal={JOURNAL OF MULTIVARIATE ANALYSIS}, author={Ni, Xiao and Zhang, Hao Helen and Zhang, Daowen}, year={2009}, month={Oct}, pages={2100–2111} }
 @article{ni_zhang_zhang_2010, title={Variable Selection for Semiparametric Mixed Models in Longitudinal Studies}, volume={66}, ISSN={["1541-0420"]}, DOI={10.1111/j.1541-0420.2009.01240.x}, abstractNote={SummaryWe propose a double‐penalized likelihood approach for simultaneous model selection and estimation in semiparametric mixed models for longitudinal data. Two types of penalties are jointly imposed on the ordinary log‐likelihood: the roughness penalty on the nonparametric baseline function and a nonconcave shrinkage penalty on linear coefficients to achieve model sparsity. Compared to existing estimation equation based approaches, our procedure provides valid inference for data with missing at random, and will be more efficient if the specified model is correct. Another advantage of the new procedure is its easy computation for both regression components and variance parameters. We show that the double‐penalized problem can be conveniently reformulated into a linear mixed model framework, so that existing software can be directly used to implement our method. For the purpose of model inference, we derive both frequentist and Bayesian variance estimation for estimated parametric and nonparametric components. Simulation is used to evaluate and compare the performance of our method to the existing ones. We then apply the new method to a real data set from a lactation study.}, number={1}, journal={BIOMETRICS}, author={Ni, Xiao and Zhang, Daowen and Zhang, Hao Helen}, year={2010}, month={Mar}, pages={79–88} }
 @article{rasheed_ni_vattam_2005, title={Comparison of methods for developing dynamic reduced models for design optimization}, volume={9}, number={1}, journal={Soft Computing (Berlin, Germany)}, author={Rasheed, K. and Ni, X. and Vattam, S.}, year={2005}, pages={29–37} }