Shaolin Ji

1.6k total citations
59 papers, 783 citations indexed

About

Shaolin Ji is a scholar working on Finance, Management Science and Operations Research and Economics and Econometrics. According to data from OpenAlex, Shaolin Ji has authored 59 papers receiving a total of 783 indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Finance, 29 papers in Management Science and Operations Research and 16 papers in Economics and Econometrics. Recurrent topics in Shaolin Ji's work include Stochastic processes and financial applications (44 papers), Risk and Portfolio Optimization (24 papers) and Insurance, Mortality, Demography, Risk Management (15 papers). Shaolin Ji is often cited by papers focused on Stochastic processes and financial applications (44 papers), Risk and Portfolio Optimization (24 papers) and Insurance, Mortality, Demography, Risk Management (15 papers). Shaolin Ji collaborates with scholars based in China, United States and United Kingdom. Shaolin Ji's co-authors include Larry G. Epstein, Mingshang Hu, Shigē Péng, Yongsheng Song, Xun Yu Zhou, Zhiyong Yu, Xiang Zhang, Jing Hao, Yue Mu and Chunhai Hao and has published in prestigious journals such as Automatica, Review of Financial Studies and Operations Research.

In The Last Decade

Shaolin Ji

53 papers receiving 722 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Shaolin Ji China 15 635 360 227 211 57 59 783
Huyên Pham France 16 916 1.4× 310 0.9× 488 2.1× 176 0.8× 61 1.1× 30 1.2k
Thilo Meyer‐Brandis Norway 14 649 1.0× 159 0.4× 316 1.4× 125 0.6× 37 0.6× 47 841
Bruno Bouchard France 18 1.1k 1.8× 384 1.1× 454 2.0× 246 1.2× 110 1.9× 70 1.3k
Jin Ma United States 15 669 1.1× 161 0.4× 161 0.7× 194 0.9× 63 1.1× 33 726
Łukasz Delong Poland 13 390 0.6× 172 0.5× 214 0.9× 317 1.5× 41 0.7× 47 590
Dylan Possamaï France 15 486 0.8× 208 0.6× 264 1.2× 89 0.4× 28 0.5× 52 616
Jingtao Shi China 16 559 0.9× 231 0.6× 179 0.8× 194 0.9× 30 0.5× 80 730
Giulia Di Nunno Norway 11 627 1.0× 169 0.5× 206 0.9× 157 0.7× 87 1.5× 54 783
Damien Lamberton France 16 914 1.4× 171 0.5× 325 1.4× 165 0.8× 83 1.5× 30 1.1k
Stéphane Crépey France 19 891 1.4× 185 0.5× 191 0.8× 139 0.7× 56 1.0× 67 1.0k

Countries citing papers authored by Shaolin Ji

Since Specialization
Citations

This map shows the geographic impact of Shaolin Ji's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shaolin Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shaolin Ji more than expected).

Fields of papers citing papers by Shaolin Ji

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shaolin Ji. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shaolin Ji. The network helps show where Shaolin Ji may publish in the future.

Co-authorship network of co-authors of Shaolin Ji

This figure shows the co-authorship network connecting the top 25 collaborators of Shaolin Ji. A scholar is included among the top collaborators of Shaolin Ji based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shaolin Ji. Shaolin Ji is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ji, Shaolin, et al.. (2024). The Neyman–Pearson lemma for convex expectations. Mathematical Control and Related Fields. 15(1). 143–159.
2.
Ji, Shaolin, et al.. (2024). Reweighted Nadaraya–Watson estimation of stochastic volatility jump-diffusion models. Computers & Mathematics with Applications. 174. 352–360. 1 indexed citations
3.
Ji, Shaolin, et al.. (2024). Mean-variance portfolio selection with non-linear wealth dynamics and random coefficients. ESAIM Control Optimisation and Calculus of Variations. 30. 45–45.
4.
Cohen, Samuel N., et al.. (2023). Reflected backward stochastic difference equations and optimal stopping problems under g-expectation. Electronic Journal of Probability. 28(none).
5.
Ji, Shaolin, et al.. (2022). Solving BSDEs based on novel multi-step schemes and multilevel Monte Carlo. Journal of Computational and Applied Mathematics. 417. 114543–114543. 1 indexed citations
6.
Hu, Mingshang, et al.. (2022). Optimization Under Rational Expectations: A Framework of Fully Coupled Forward-Backward Stochastic Linear Quadratic Systems. Mathematics of Operations Research. 48(3). 1767–1790. 3 indexed citations
7.
Ji, Shaolin, et al.. (2022). A Modified Method of Successive Approximations for Stochastic Recursive Optimal Control Problems. SIAM Journal on Control and Optimization. 60(5). 2759–2786. 2 indexed citations
8.
Ji, Shaolin, et al.. (2020). Deep learning method for solving stochastic optimal control problem via stochastic maximum principle.. arXiv (Cornell University). 1 indexed citations
9.
Ji, Shaolin, et al.. (2019). A stochastic maximum principle for linear quadratic problem with nonconvex control domain. Mathematical Control and Related Fields. 9(3). 495–507. 2 indexed citations
10.
Ji, Shaolin, et al.. (2017). The least squares estimator of random variables under sublinear expectations. Journal of Mathematical Analysis and Applications. 451(2). 906–923. 8 indexed citations
11.
Hu, Mingshang & Shaolin Ji. (2016). Dynamic programming principle for stochastic recursive optimal control problem driven by a G-Brownian motion. Stochastic Processes and their Applications. 127(1). 107–134. 22 indexed citations
12.
Zhang, Hongsheng, Xiang Zhang, Shaolin Ji, et al.. (2014). Sohlh2 inhibits ovarian cancer cell proliferation by upregulation of p21 and downregulation of cyclin D1. Carcinogenesis. 35(8). 1863–1871. 26 indexed citations
13.
Ji, Shaolin, et al.. (2013). A maximum principle for fully coupled forward–backward stochastic control systems with terminal state constraints. Journal of Mathematical Analysis and Applications. 407(2). 200–210. 14 indexed citations
14.
Hu, Mingshang, Shaolin Ji, Shigē Péng, & Yongsheng Song. (2013). Backward stochastic differential equations driven byG-Brownian motion. Stochastic Processes and their Applications. 124(1). 759–784. 90 indexed citations
15.
Hu, Mingshang, Shaolin Ji, Shigē Péng, & Yongsheng Song. (2013). Comparison theorem, Feynman–Kac formula and Girsanov transformation for BSDEs driven byG-Brownian motion. Stochastic Processes and their Applications. 124(2). 1170–1195. 83 indexed citations
16.
Ji, Shaolin & Xun Yu Zhou. (2009). A generalized Neyman–Pearson lemma for g-probabilities. Probability Theory and Related Fields. 148(3-4). 645–669. 13 indexed citations
17.
Ji, Shaolin & Shigē Péng. (2007). Terminal perturbation method for the backward approach to continuous time mean–variance portfolio selection. Stochastic Processes and their Applications. 118(6). 952–967. 15 indexed citations
18.
Ji, Shaolin & Xun Yu Zhou. (2006). A maximum principle for stochastic optimal control with terminal state constraints, and its applications. Communications in Information and Systems. 6(4). 321–338. 53 indexed citations
19.
Ji, Shaolin, et al.. (2005). Sampling schedule design towards optimal drug monitoring for individualizing therapy. Computer Methods and Programs in Biomedicine. 80(1). 57–63. 2 indexed citations
20.
Ji, Shaolin. (2001). Dynamic Measure of Risk and a Related Stochastic Game Problem. Mathematica Applicata. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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