X. Sheldon Lin

3.9k citations
100 papers · 2.7k indexed · h-index 27

X. Sheldon Lin

97 papers receiving 2.6k citations

Peers

X. Sheldon Lin
Comparison fields: 5 of 148
  • Management Science and Operations Research 1.3k
  • Statistics and Probability 690
  • Demography 949
  • Finance 628
  • Artificial Intelligence 665
Replace Kai Wang Ng with:
Kai Wang Ng Hong Kong
Minyi Huang Canada
Tomasz Rolski Poland
Upendra Dave India
Ludger Rüschendorf Germany
Richard L. Dykstra United States
Perwez Shahabuddin United States
Ernst Eberlein Germany
Sanat K. Sarkar United States
Miklós Csörgő Canada
X. Sheldon Lin relative to Kai Wang Ng Hong Kong Kai Wang Ng's profile →
Citations per field
00.5×2.8×
Kai Wang Ng · 1×
Citations per year

Countries citing papers authored by X. Sheldon Lin

Since Specialization
Citations

This map shows the geographic impact of X. Sheldon Lin'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 X. Sheldon Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites X. Sheldon Lin more than expected).

Fields of papers citing papers by X. Sheldon Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by X. Sheldon Lin. 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 X. Sheldon Lin. The network helps show where X. Sheldon Lin may publish in the future.

Co-authorship network

The 25 scholars most cited alongside X. Sheldon Lin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with X. Sheldon Lin Line = papers co-authored together X. Sheldon Lin links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20252
3 202414
4 20226
5 201822
6 20184
7 201410
8 20125
9 20115
10 2010109
11 201099
12 200912
13
Introducing people knowledge into science learning
20083
14 200811
15 20079
16 200662
17 200611
18
The effect of multiple-perspective thinking on problem solving
20063
19
The ideal science student and problem solving
20063
20 2003167

About X. Sheldon Lin

X. Sheldon Lin is a scholar working on Statistics and Probability, Management Science and Operations Research, Demography, Finance and Toxicology, having authored 100 papers that have together received 2.7k indexed citations. Recurring topics across this work include Probability and Risk Models (29 papers), Insurance, Mortality, Demography, Risk Management (25 papers), Statistical Distribution Estimation and Applications (23 papers), Bayesian Methods and Mixture Models (17 papers), Stochastic processes and financial applications (11 papers), Statistical Methods and Bayesian Inference (11 papers), Insurance and Financial Risk Management (9 papers) and Statistical Methods and Inference (7 papers). The work is most often cited by research in Management Science and Operations Research (1.3k citations), Statistics and Probability (690 citations), Demography (949 citations), Finance (628 citations) and Artificial Intelligence (665 citations). X. Sheldon Lin has collaborated with scholars based in Canada, United States and China. Frequent co-authors include Gordon E. Willmot, Steve Drekic, Hao Helen Zhang, Cheolwoo Park, Jeongyoun Ahn, Ken Seng Tan, Hailiang Yang, Andrei L. Badescu, Cindy E. Hmelo and Chris Clifton. Their work appears in journals such as Insurance Mathematics and Economics, North American Actuarial Journal, Astin Bulletin, Journal of Applied Probability and Journal of Mathematical Biology.

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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