Shan Lu

Orcid: 0000-0002-8263-8598

Affiliations:
  • Central University of Finance and Economics, School of Statistics and Mathematics, Beijing, China
  • Beihang University, School of Economics and Management, Beijing, China (PhD 2019)


According to our database1, Shan Lu authored at least 15 papers between 2019 and 2025.

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

Timeline

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Bibliography

2025
Graphical model for mixed data types.
Neurocomputing, 2025

2024
IDGM: an approach to estimate the graphical model of interval-valued data.
Stat. Comput., December, 2024

Nonlinear directed acyclic graph estimation based on the kernel partial correlation coefficient.
Inf. Sci., January, 2024

Kent feature embedding for classification of compositional data with zeros.
Stat. Comput., 2024

2023
Skeleton estimation of directed acyclic graphs using partial least squares from correlated data.
Pattern Recognit., July, 2023

What matters for short videos' user engagement: A multiblock model with variable screening.
Expert Syst. Appl., May, 2023

Unraveling Effects of Health Short Videos' Instructor Movement Features on Viewer Engagement: An Interval-valued Data Analysis.
Proceedings of the 27th Pacific Asia Conference on Information Systems, 2023

2022
M-LDQ feature embedding and regression modeling for distribution-valued data.
Inf. Sci., 2022

Academic failures and co-location social networks in campus.
EPJ Data Sci., 2022

2021
MD-MBPLS: A novel explanatory model in computational social science.
Knowl. Based Syst., 2021

A classification framework for multivariate compositional data with Dirichlet feature embedding.
Knowl. Based Syst., 2021

Risk spillover network structure learning for correlated financial assets: A directed acyclic graph approach.
Inf. Sci., 2021

An effective intrusion detection approach using SVM with naïve Bayes feature embedding.
Comput. Secur., 2021

2020
Trading Imbalance in Chinese Stock Market - A High-Frequency View.
Entropy, 2020

2019
Aggregating multiple types of complex data in stock market prediction: A model-independent framework.
Knowl. Based Syst., 2019


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