@article{luo_song_styner_gilmore_zhu_2018, title={FSEM: Functional Structural Equation Models for Twin Functional Data}, volume={114}, ISSN={0162-1459 1537-274X}, url={http://dx.doi.org/10.1080/01621459.2017.1407773}, DOI={10.1080/01621459.2017.1407773}, abstractNote={ABSTRACT The aim of this article is to develop a novel class of functional structural equation models (FSEMs) for dissecting functional genetic and environmental effects on twin functional data, while characterizing the varying association between functional data and covariates of interest. We propose a three-stage estimation procedure to estimate varying coefficient functions for various covariates (e.g., gender) as well as three covariance operators for the genetic and environmental effects. We develop an inference procedure based on weighted likelihood ratio statistics to test the genetic/environmental effect at either a fixed location or a compact region. We also systematically carry out the theoretical analysis of the estimated varying functions, the weighted likelihood ratio statistics, and the estimated covariance operators. We conduct extensive Monte Carlo simulations to examine the finite-sample performance of the estimation and inference procedures. We apply the proposed FSEM to quantify the degree of genetic and environmental effects on twin white matter tracts obtained from the UNC early brain development study. Supplementary materials for this article are available online.}, number={525}, journal={Journal of the American Statistical Association}, publisher={Informa UK Limited}, author={Luo, S. and Song, R. and Styner, M. and Gilmore, J. H. and Zhu, H.}, year={2018}, month={Jul}, pages={344–357} }
 @article{song_luo_zeng_zhang_lu_li_2017, title={Semiparametric single-index model for estimating optimal individualized treatment strategy}, volume={11}, ISSN={["1935-7524"]}, DOI={10.1214/17-ejs1226}, abstractNote={Different from the standard treatment discovery framework which is used for finding single treatments for a homogenous group of patients, personalized medicine involves finding therapies that are tailored to each individual in a heterogeneous group. In this paper, we propose a new semiparametric additive single-index model for estimating individualized treatment strategy. The model assumes a flexible and nonparametric link function for the interaction between treatment and predictive covariates. We estimate the rule via monotone B-splines and establish the asymptotic properties of the estimators. Both simulations and an real data application demonstrate that the proposed method has a competitive performance.}, number={1}, journal={ELECTRONIC JOURNAL OF STATISTICS}, author={Song, Rui and Luo, Shikai and Zeng, Donglin and Zhang, Hao Helen and Lu, Wenbin and Li, Zhiguo}, year={2017}, pages={364–384} }
 @article{luo_ghosal_2016, title={Forward selection and estimation in high dimensional single index models}, volume={33}, ISSN={1572-3127}, url={http://dx.doi.org/10.1016/J.STAMET.2016.09.002}, DOI={10.1016/j.stamet.2016.09.002}, abstractNote={We propose a new variable selection and estimation technique for high dimensional single index models with unknown monotone smooth link function. Among many predictors, typically, only a small fraction of them have significant impact on prediction. In such a situation, more interpretable models with better prediction accuracy can be obtained by variable selection. In this article, we propose a new penalized forward selection technique which can reduce high dimensional optimization problems to several one dimensional optimization problems by choosing the best predictor and then iterating the selection steps until convergence. The advantage of optimizing in one dimension is that the location of optimum solution can be obtained with an intelligent search by exploiting smoothness of the criterion function. Moreover, these one dimensional optimization problems can be solved in parallel to reduce computing time nearly to the level of the one-predictor problem. Numerical comparison with the LASSO and the shrinkage sliced inverse regression shows very promising performance of our proposed method.}, journal={Statistical Methodology}, publisher={Elsevier BV}, author={Luo, Shikai and Ghosal, Subhashis}, year={2016}, month={Dec}, pages={172–179} }
 @article{luo_ghosal_2015, title={Prediction consistency of forward iterated regression and selection technique}, volume={107}, ISSN={0167-7152}, url={http://dx.doi.org/10.1016/J.SPL.2015.08.005}, DOI={10.1016/j.spl.2015.08.005}, abstractNote={Recently, Hwang et al. (2009) introduced a penalized forward selection technique for high dimensional linear regression which appears to possess excellent prediction and variable selection properties. In this article, we show that the procedure is prediction consistent.}, journal={Statistics & Probability Letters}, publisher={Elsevier BV}, author={Luo, Shikai and Ghosal, Subhashis}, year={2015}, month={Dec}, pages={79–83} }
 @article{lee_steiner_luo_neale_styner_zhu_gilmore_2015, title={Quantitative tract-based white matter heritability in twin neonates}, volume={111}, ISSN={["1095-9572"]}, DOI={10.1016/j.neuroimage.2015.02.021}, abstractNote={Studies in adults indicate that white matter microstructure, assessed with diffusion tensor imaging (DTI), has high heritability. Little is known about genetic and environmental influences on DTI parameters, measured along fiber tracts particularly, in early childhood. In the present study, we report comprehensive heritability data of white matter microstructure fractional anisotropy (FA), radial diffusion (RD), and axial diffusion (AD) along 47 fiber tracts using the quantitative tractography in a large sample of neonatal twins (n=356). We found significant genetic influences in almost all tracts with similar heritabilities for FA, RD, and AD as well as positive relationships between these parameters and heritability. In a single tract analysis, genetic influences along the length of the tract were highly variable. These findings suggest that at birth, there is marked heterogeneity of genetic influences of white matter microstructure within white matter tracts. This study provides a basis for future studies of developmental changes in genetic and environmental influences during early childhood, a period of rapid development that likely plays a major role in individual differences in white matter structure and function.}, journal={NEUROIMAGE}, author={Lee, Seung Jae and Steiner, Rachel J. and Luo, Shikai and Neale, Michael C. and Styner, Martin and Zhu, Hongtu and Gilmore, John H.}, year={2015}, month={May}, pages={123–135} }