Naijun Sha

1.2k citations
26 papers · 792 · h-index 13

Impact in

Papers in

Naijun Sha

24 papers receiving 755 citations

Peers

Naijun Sha
Comparison fields: 5 of 98
  • Statistics and Probability 300
  • Statistics, Probability and Uncertainty 78
  • Artificial Intelligence 276
  • Analytical Chemistry 69
  • Safety, Risk, Reliability and Quality 61
Replace Hojin Moon with:
Hojin Moon United States
Baoxue Zhang China
Harold B. Sackrowitz United States
Xiangrong Yin United States
Chris Hans United States
Jacek Koronacki Poland
José R. Berrendero Spain
Jack C. Lee Taiwan
Naijun Sha relative to Hojin Moon United States Hojin Moon's profile →
Citations per field
00.5×8.7×
Hojin Moon · 1×
Citations per year

Countries citing papers authored by Naijun Sha

Since Specialization
Citations

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

Fields of papers citing papers by Naijun Sha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Naijun Sha. 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 Naijun Sha. The network helps show where Naijun Sha may publish in the future.

Co-authors

The 19 scholars most cited alongside Naijun Sha, 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 Naijun Sha Line = papers co-authored together Naijun Sha links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2002257
2 2005155
3 200490
4 200664
5 200543
6 201339
7 200416
8 200315
9 201513
10
Identifying biomarkers from mass spectrometry data with ordinal outcome.
200713
11 200712
12 201712
13 201212
14 20149
15 20188
16 20158
17 20177
18 20125
19 20194
20 20074

About Naijun Sha

Naijun Sha is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty, Safety, Risk, Reliability and Quality, Molecular Biology and Artificial Intelligence, having authored 26 papers that have together received 792 indexed citations. Recurring topics across this work include Statistical Distribution Estimation and Applications (13 papers), Reliability and Maintenance Optimization (8 papers), Probabilistic and Robust Engineering Design (7 papers), Statistical Methods and Inference (7 papers), Gene expression and cancer classification (7 papers), Spectroscopy and Chemometric Analyses (4 papers), Bayesian Methods and Mixture Models (4 papers) and Advanced Statistical Process Monitoring (3 papers). The work is most often cited by research in Statistics and Probability (300 citations), Statistics, Probability and Uncertainty (78 citations), Artificial Intelligence (276 citations), Analytical Chemistry (69 citations) and Safety, Risk, Reliability and Quality (61 citations). Naijun Sha has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Marina Vannucci, Mahlet G. Tadesse, Bani K. Mallick, Edward R. Dougherty, Philip J. Brown, Rong Pan, Francesco Falciani, I. Dragoni, Andrea Contestabile and Hua Liang. Their work appears in journals such as Bioinformatics, Chemometrics and Intelligent Laboratory Systems, IEEE Transactions on Reliability, Journal of Statistical Computation and Simulation and Mathematical Biosciences.

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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