* indicates my students.
Preprints
[-]. Sang, Peijun & Li, Bing. Nonlinear Function-on-function Regression by RKHS. arXiv:2207.08211.
[-]. Sang, Peijun, Shang, Zuofeng & Du, Pang. Statistical Inference for Functional Linear Quantile Regression. arXiv:2202.11747.
[-]. Luo, Yao & Sang, Peijun. Penalized Sieve Estimation of Structural Models. arXiv:2204.13488.
[-]. Zhang, Chi*, Sang, Peijun & Qin, Yingli. Two-sample Inference for Sparse Functional Data. arXiv:2312.07727.
[-]. Jian, Jie*, Zhu, Mu & Sang, Peijun. Restricted Tweedie Stochastic Block Models. arXiv:2310.10952.
[-]. Shang, Zuofeng, Sang, Peijun, Yang, Feng & Jin, Chong. Variational Nonparametric Inference in Functional Stochastic Block Model. arXiv:2407.00564.
Statistical Methodology Papers
[1]. Joe, H. & Sang, P. (2016). Multivariate Models for Dependent Clusters of Variables with Conditional Independence given Aggregation Variables. Computational Statistics & Data Analysis, 97, 114–132.
[2]. Sang, P., Wang, L. & Cao, J. (2017). Parametric Functional Principal Component Analysis. Biometrics, 73, 802–810.
[3]. Sang P., Lochhart, R.A. & Cao, J. (2018). Sparse Estimation for Functional Semiparametric Additive Models. Journal of Multivariate Analsis, 168, 105–118.
[4]. Sang, P., Wang, L. & Cao, J. (2019) Weighted Empirical Likelihood Inference for Dynamical Correlations. Computational Statistics & Data Analysis, 131, 194–206.
[5]. Sang, P. & Cao, J. (2020). Functional Single-index Quantile Regression Models. Statistics and Computing, 30, 771–781.
[6]. Sang, P., Wang, L. & Cao, J. (2020). Estimation of Sparse Functional Additive Models with Adaptive Group LASSO. Statistica Sinica, 30, 1191–1211.
[7]. Sang, P., Begen, M.A. & Cao, J. (2021). Appointment Scheduling with a Quantile Objective. Computers and Operations Research, 132, 105295.
[8]. Zhou. Z. & Sang, P. (2022) Continuum Centroid Classifier for Functional Data. Candian Journal of Statistics, 50, 200–220.
[9]. Shang, H.L., Cao, J. & Sang, P. (2022). Stopping Time Detection of Wood Panel Compression: a Functional Time-series Approach. Journal of the Royal Statistical Society, Series C, 71, 1205–1224.
[10]. Liu, B.* & Sang, P. (2022). L1-regularized Functional Support Vector Machine. Statistics and Its Interface, 17, 349–356.
[11]. Sang, P., Kashlak, A.B. & Kong, L. (2023) Reproducing Kernel Hilbert Space Framework for Functional Classification. Journal of Computational and Graphical Statistics, 30, 1000–1008.
[12]. Yeh, C.-K. & Sang, P. (2023). Variable Selection in Multivariate Functional Linear Regression. Statsitics in Bioscience.
[13]. Sang, P. (2023). Distance‐weighted Discrimination for Functional Data. Stat, 2:e598.
[14]. Jian, J.*, Sang, P. & Zhu, M. (2024). Two Gaussian Regularization Methods for Time-varying Networks. Journal of Agricultural, Biological and Environmental Statistics, 29, 853–873.
[15]. Xu, M.*, Wong, S.W.K. & Sang, P. (2024). A Bayesian Collocation Integral Method for Parameter Estimation in Ordinary Differential Equations. Journal of Computational and Graphical Statistics, 33, 845–854.
[16]. Luo, Y., Sang, P. & Xiao R. (2024). Order Statistics Approaches to Unobserved Heterogeneity in Auctions. Electronic Journal of Statistics, 18, 2477–2530.
[17]. Sang, P., Kong, D. & Yang, S. (2024). Functional Principal Component Analysis with informative observation time. Biometrika, in press.
Application Papers
[1]. Yu, H., Sang, P. & Huan, T. (2022). Adaptive Box-Cox transformation: a highly flexible feature-specific data transformation to improve metabolomic data normality for better statistical analysis. Analytical Chemistry, 94, 8267-8276.
Others
[1]. Sang, P., Nie, Y. & Cao, J. (2016). Comments on: Probability enhanced effective dimension reduction for classifying sparse functional data. Test, 25, 33-34.