Shanshan Wang

Orcid: 0000-0002-7205-3844

Affiliations:
  • Beihang University, School of Economics and Management, Beijing, China
  • Beijing Normal University, China (PhD 2014)


According to our database1, Shanshan Wang authored at least 14 papers between 2017 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Neural network for censored expectile regression based on data augmentation.
Neurocomputing, 2026

2024
Variable selection for multivariate functional data via conditional correlation learning.
Comput. Stat., June, 2024

2023
Robust regression for interval-valued data based on midpoints and log-ranges.
Adv. Data Anal. Classif., September, 2023

2021
Convex clustering method for compositional data via sparse group lasso.
Neurocomputing, 2021

A flexible spatial autoregressive modelling framework for mixed covariates of multiple data types.
Commun. Stat. Simul. Comput., 2021

A robust spatial autoregressive scalar-on-function regression with t-distribution.
Adv. Data Anal. Classif., 2021

2020
Ultra-high dimensional variable screening via Gram-Schmidt orthogonalization.
Comput. Stat., 2020

2019
Linear mixed-effects model for multivariate longitudinal compositional data.
Neurocomputing, 2019

Bayesian quantile regression and variable selection for partial linear single-index model: Using free knot spline.
Commun. Stat. Simul. Comput., 2019

Incremental modelling for compositional data streams.
Commun. Stat. Simul. Comput., 2019

A novel approach to intrusion detection using SVM ensemble with feature augmentation.
Comput. Secur., 2019

2018
Semiparametric regression analysis of clustered survival data with semi-competing risks.
Comput. Stat. Data Anal., 2018

2017
Penalized empirical likelihood inference for sparse additive hazards regression with a diverging number of covariates.
Stat. Comput., 2017

An effective intrusion detection framework based on SVM with feature augmentation.
Knowl. Based Syst., 2017


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