Jiajuan Liang

607 citations
39 papers · 323 indexed · h-index 10

Jiajuan Liang

36 papers receiving 311 citations

Peers

Jiajuan Liang
Comparison fields: 5 of 93
  • Statistics and Probability 177
  • Statistics, Probability and Uncertainty 47
  • Management Science and Operations Research 47
  • Finance 25
  • Environmental Engineering 33
Replace Hong-Tu Zhu with:
Hong-Tu Zhu Hong Kong
A. W. Bowman United Kingdom
Hung Chen Taiwan
Pavlo Mozharovskyi France
Zahra Almaspoor Iran
Christophe Crambes France
Lu Lin China
Daniel Vogel Germany
Gabriel A. Rodriguez‐Yam Mexico
Eduardo Gutiérrez‐Peña Mexico
Jiajuan Liang relative to Hong-Tu Zhu Hong Kong Hong-Tu Zhu's profile →
Citations per field
00.5×3.2×
Hong-Tu Zhu · 1×
Citations per year

Countries citing papers authored by Jiajuan Liang

Since Specialization
Citations

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

Fields of papers citing papers by Jiajuan Liang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Jiajuan Liang, 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 Jiajuan Liang Line = papers co-authored together Jiajuan Liang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20241
3 20240
4 20243
5 20223
6 20204
7 20201
8 20189
9 20171
10 20161
11
A New EM Algorithm for Maximum Likelihood Estimation in Two-level Structural Equation Models with Arbitrary Sample Designs
20111
12 20114
13 200930
14 20082
15 200714
16 20061
17 20066
18 200452
19 200030
20 19987

About Jiajuan Liang

Jiajuan Liang is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty, Numerical Analysis, Analytical Chemistry and Environmental Engineering, having authored 39 papers that have together received 323 indexed citations. Recurring topics across this work include Advanced Statistical Methods and Models (18 papers), Statistical Methods and Inference (13 papers), Statistical Distribution Estimation and Applications (12 papers), Statistical Methods and Bayesian Inference (6 papers), Bayesian Methods and Mixture Models (6 papers), Probabilistic and Robust Engineering Design (5 papers), Advanced Statistical Process Monitoring (5 papers) and Spectroscopy and Chemometric Analyses (5 papers). The work is most often cited by research in Statistics and Probability (177 citations), Statistics, Probability and Uncertainty (47 citations), Management Science and Operations Research (47 citations), Finance (25 citations) and Environmental Engineering (33 citations). Jiajuan Liang has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Peter M. Bentler, Runze Li, Man‐Lai Tang, Kai‐Tai Fang, Fred J. Hickernell, Kai‐Tai Fang, Kai-Tai Fang, Ping Shing Chan, Hong‐Bin Fang and Kai Wang Ng. Their work appears in journals such as Computational Statistics & Data Analysis, Annals of the Institute of Statistical Mathematics, Journal of Computational and Graphical Statistics, Journal of Statistical Computation and Simulation and Psychometrika.

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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