Lan Wang

发表

CALIBRATING NON-CONVEX PENALIZED REGRESSION IN ULTRA-HIGH DIMENSION.

Runze Li, Yongdai Kim, Lan Wang, 2013, Annals of statistics.

Variable selection for support vector machines in moderately high dimensions

Runze Li, Yichao Wu, Lan Wang, 2016, Journal of the Royal Statistical Society. Series B, Statistical methodology.

An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression

Bo Peng, Lan Wang, 2015 .

Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension

Runze Li, Yichao Wu, Lan Wang, 2012, Journal of the American Statistical Association.

Nonparametric Test for the Form of Parametric Regression with Time Series Errors

I. Keilegom, Lan Wang, 2007 .

Weighted Wilcoxon‐Type Smoothly Clipped Absolute Deviation Method

Runze Li, Lan Wang, 2009, Biometrics.

Robust Tests in Regression Models With Omnibus Alternatives and Bounded Influence

A. Qu, Lan Wang, 2007 .

Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data

Lan Wang, Hyokyoung Grace Hong, Xuming He, 2013, 1304.2186.

Partially linear additive quantile regression in ultra-high dimension

Lan Wang, Ben Sherwood, Lan Wang, 2016, 1601.06000.

Correction: Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data

Xuming He, Lan Wang, H. Hong, 2013 .

An ANOVA-type nonparametric diagnostic test for heteroscedastic regression models

Lan Wang, Michael G. Akritas, Ingrid Van Keilegom, 2008 .

A Parallel Algorithm for Large-Scale Nonconvex Penalized Quantile Regression

Lan Wang, Liqun Yu, Lan Wang, 2017 .

Envelope Quantile Regression

Shanshan Ding, Lan Wang, G. Zhu, 2020, Statistica Sinica.

Consistent model selection and data‐driven smooth tests for longitudinal data in the estimating equations approach

Annie Qu, Lan Wang, A. Qu, 2009 .

Summary. Model selection for marginal regression analysis of longitudinal data is challenging owing to the presence of correlation and the difficulty of specifying the full likelihood, particularly

A. Qu, Lan Wang, 2009 .

Semiparametric modeling and estimation of the dispersion function in regression

I. Keilegom, Lan Wang, 2008 .

TWO-WAY HETEROSCEDASTIC ANOVA WHEN THE NUMBER OF LEVELS IS LARGE

M. Akritas, Lan Wang, 2006 .

Variable Selection for Support Vector Machines in High Dimensions

Yichao Wu, Lan Wang, X. Zhang, 2014 .

Wild residual bootstrap inference for penalized quantile regression with heteroscedastic errors

I. Van Keilegom, Lan Wang, Adam Maidman, 2018, Biometrika.

Double-slicing assisted sufficient dimension reduction for high-dimensional censored data

Shanshan Ding, Lan Wang, W. Qian, 2020 .

Semiparametric modeling and estimation of heteroscedasticity in regression analysis of cross-sectional data

I. Keilegom, Lan Wang, 2010 .

Discussion

Lan Wang, Ben Sherwood, 2016 .

Supplement to “A Tuning-free Robust and Eﬃcient Approach to High-dimensional Regression”

Bo Peng, Lan Wang, Jelena Bradic, 2020 .

Quantile regression analysis of length‐biased survival data

Lan Wang, H. Wang, 2014 .

Online Supplement to JASA-T&M-2015-0234R2 (cid:16)Quantile-Optimal Treatment Regimes"

Rui Song, Lan Wang, Yu Zhou, 2017 .

ANALYSIS OF GLOBAL AND LOCAL OPTIMA OF REGULARIZED QUANTILE REGRESSION IN HIGH DIMENSIONS: A SUBGRADIENT APPROACH

Xuming He, Lan Wang, 2022, Econometric Theory.

Nonparametric goodness-of-fit test for heteroscedastic regression models

M. Akritas, I. Keilegom, Lan Wang, 2002 .

High‐dimensional quantile regression: Convolution smoothing and concave regularization

Kean Ming Tan, Lan Wang, Wen-Xin Zhou, 2021, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces

Xiang Zhang, Runze Li, Lan Wang, 2016, J. Mach. Learn. Res..

Transformation-Invariant Learning of Optimal Individualized Decision Rules with Time-to-Event Outcomes

Rui Song, Lan Wang, Yu Zhou, 2022, Journal of the American Statistical Association.

Locally Weighted Censored Quantile Regression

Lan Wang, Huixia Judy Wang, Lan Wang, 2009 .

GEE analysis of clustered binary data with diverging number of covariates

Lan Wang, Lan Wang, 2011, 1103.1795.

Censored quantile regression with recursive partitioning-based weights.

Lan Wang, K. Rudser, A. Wey, 2014, Biostatistics.

Generalized additive partial linear models for clustered data with diverging number of covariates using gee

Heng Lian, Hua Liang, Lan Wang, 2014 .

Using quantile regression to create baseline norms for neuropsychological tests

Lan Wang, Sandra Weintraub, Ben Sherwood, 2015, Alzheimer's & dementia.

Quantile-Optimal Treatment Regimes

Rui Song, Ben Sherwood, Lan Wang, 2018, Journal of the American Statistical Association.

Estimation and Inference of Quantile Regression for Survival Data Under Biased Sampling

Chiung-Yu Huang, Gongjun Xu, Lan Wang, 2017, Journal of the American Statistical Association.

Statistica Sinica Preprint No : SS-2018-0060 Title Envelope Quantile Regression

Lan Wang, G. Zhu, 2019 .

A High-Dimensional Nonparametric Multivariate Test for Mean Vector

Runze Li, Lan Wang, Runze Li, 2015, Journal of the American Statistical Association.

A Survey of Tuning Parameter Selection for High-dimensional Regression pdf

Yunan Wu, Lan Wang, Lan Wang, 2019, Annual Review of Statistics and Its Application.

Web-based Supplementary Materials for “Comparing Large Covariance Matrices under Weak Conditions on the Dependence Structure and its Application to Gene Clustering”,

Wen-Xin Zhou, Wen Zhou, Jinyuan Chang, 2016 .

Resampling‐based confidence intervals for model‐free robust inference on optimal treatment regimes

Yunan Wu, Lan Wang, Lan Wang, 2019, Biometrics.

Penalized Generalized Estimating Equations for High‐Dimensional Longitudinal Data Analysis

Annie Qu, Lan Wang, Jianhui Zhou, 2012, Biometrics.

Quantile partially linear additive model for data with dropouts and an application to modeling cognitive decline

Lan Wang, Ben Sherwood, Adam Maidman, 2023, Statistics in medicine.

New semiparametric method for predicting high‐cost patients

Lan Wang, Adam Maidman, 2018, Biometrics.

Weighted quantile regression for analyzing health care cost data with missing covariates

Lan Wang, Xiao-Hua Zhou, Ben Sherwood, 2013, Statistics in medicine.

Model-Assisted Uniformly Honest Inference for Optimal Treatment Regimes in High Dimension

Haoda Fu, Yunan Wu, Lan Wang, 2021, Journal of the American Statistical Association.

Penalized profiled semiparametric estimating functions

Chih-Ling Tsai, Lan Wang, C. Heuchenne, 2013 .

Semiparametric Estimating Functions

Chih-Ling Tsai, Lan Wang, Bo Kai, 2013 .

A nonparametric doubly robust test for a continuous treatment effect

Charles R. Doss, Lan Wang, I. Moscovice, 2022, 2202.03369.

Robust and Tuning-Free Sparse Linear Regression via Square-Root Slope

Stanislav Minsker, Lan Wang, M. Ndaoud, 2022, 2210.16808.

Quantile Off-Policy Evaluation via Deep Conditional Generative Learning

Lan Wang, C. Shi, R. Song, 2022, ArXiv.

PGEE: An R Package for Analysis of Longitudinal Data with High-Dimensional Covariates

Gul Inan, Lan Wang, Lan Wang, 2017, R J..

Wilcoxon-type generalized Bayesian information criterion

Lan Wang, Lan Wang, 2009 .

Testing for Covariate Effects in the Fully Nonparametric Analysis of Covariance Model

Lan Wang, M. Akritas, Lan Wang, 2006 .

Nonparametric test for checking lack of fit of the quantité regression model under random censoring

Lan Wang, Lan Wang, 2008 .

A Tuning-free Robust and Efficient Approach to High-dimensional Regression

Runze Li, Bo Peng, Yunan Wu, 2020, Journal of the American Statistical Association.

Sparse Concordance-assisted Learning for Optimal Treatment Decision

Lan Wang, Wenbin Lu, Rui Song, 2018, J. Mach. Learn. Res..

Quantile-Optimal Treatment Regimes

Rui Song, Lan Wang, Yu Zhou, 2018, Journal of the American Statistical Association.

Local Rank Inference for Varying Coefficient Models

Runze Li, Lan Wang, Runze Li, 2009, Journal of the American Statistical Association.

An Error Bound for L1-norm Support Vector Machine Coefficients in Ultra-high Dimension

Bo Peng, Lan Wang, Yichao Wu, 2016, J. Mach. Learn. Res..

Quantile Regression for Recurrent Gap Time Data

Xianghua Luo, Chiung-Yu Huang, Lan Wang, 2013, Biometrics.