Liang Wang

2.2k total citations · 1 hit paper
131 papers, 1.6k citations indexed

About

Liang Wang is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Safety, Risk, Reliability and Quality. According to data from OpenAlex, Liang Wang has authored 131 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 95 papers in Statistics and Probability, 73 papers in Statistics, Probability and Uncertainty and 28 papers in Safety, Risk, Reliability and Quality. Recurrent topics in Liang Wang's work include Statistical Distribution Estimation and Applications (86 papers), Probabilistic and Robust Engineering Design (70 papers) and Reliability and Maintenance Optimization (28 papers). Liang Wang is often cited by papers focused on Statistical Distribution Estimation and Applications (86 papers), Probabilistic and Robust Engineering Design (70 papers) and Reliability and Maintenance Optimization (28 papers). Liang Wang collaborates with scholars based in China, India and United States. Liang Wang's co-authors include Yogesh Mani Tripathi, Hongmiao Tian, Xiangming Li, Xiaoliang Chen, Yongsong Luo, Jinyou Shao, Duorui Wang, Bingheng Lu, Shouren Chen and Min Sheng and has published in prestigious journals such as Advanced Materials, SHILAP Revista de lepidopterología and Journal of the American Statistical Association.

In The Last Decade

Liang Wang

114 papers receiving 1.6k citations

Hit Papers

Flexible Capacitive Press... 2019 2026 2021 2023 2019 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Liang Wang China 21 602 477 455 424 230 131 1.6k
Ran Jin United States 19 69 0.1× 104 0.2× 166 0.4× 144 0.3× 13 0.1× 94 1.3k
Dawei Huang China 24 91 0.2× 207 0.4× 82 0.2× 138 0.3× 28 0.1× 144 1.6k
Lanqing Hong China 16 98 0.2× 32 0.1× 82 0.2× 242 0.6× 23 0.1× 41 778
Michael H. Azarian United States 25 13 0.0× 264 0.6× 93 0.2× 866 2.0× 8 0.0× 114 2.2k
S. M. Taheri Iran 14 291 0.5× 67 0.1× 42 0.1× 29 0.1× 6 0.0× 36 780
Jiajie Fan China 25 24 0.0× 218 0.5× 110 0.2× 1.2k 2.7× 2 0.0× 197 2.5k
Diganta Das United States 21 17 0.0× 112 0.2× 114 0.3× 1.0k 2.5× 4 0.0× 89 2.1k
Dong Xiang United States 10 98 0.2× 135 0.3× 23 0.1× 116 0.3× 18 0.1× 14 489
Qing Shen China 23 88 0.1× 353 0.7× 17 0.0× 460 1.1× 8 0.0× 85 1.8k
Xiaofei Zhang China 25 5 0.0× 260 0.5× 71 0.2× 592 1.4× 11 0.0× 115 2.0k

Countries citing papers authored by Liang Wang

Since Specialization
Citations

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

Fields of papers citing papers by Liang Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liang Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Liang Wang. A scholar is included among the top collaborators of Liang Wang 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 Liang Wang. Liang Wang 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.
Wang, Liang, Chunfang Zhang, Yogesh Mani Tripathi, & Yuhlong Lio. (2024). Analysis of dependent complementary competing risks data from a generalized inverted family of lifetime distributions under a maximum ranked set sampling procedure with unequal samples. Journal of Computational and Applied Mathematics. 457. 116309–116309.
2.
Tripathi, Yogesh Mani, et al.. (2024). Inference for Unit Inverse Weibull Distribution Under Block Progressive Type-II Censoring. Journal of Statistical Theory and Practice. 18(3). 1 indexed citations
3.
Tripathi, Yogesh Mani, et al.. (2024). Analysis of Block Adaptive Type-II Progressive Hybrid Censoring with Weibull Distribution. Mathematics. 12(24). 4026–4026. 1 indexed citations
4.
Chen, Yutong, Ting-Feng Yi, Junping Chen, et al.. (2024). Revisiting the quasi-periodic oscillations in blazar PG 1553+113 with multi-wavebands data. New Astronomy. 108. 102186–102186.
5.
Tripathi, Yogesh Mani, et al.. (2023). Reliability estimation for bathtub-shaped distribution under block progressive censoring. Mathematics and Computers in Simulation. 213. 237–260. 7 indexed citations
7.
Wang, Liang, et al.. (2022). Interval Estimation of Generalized Inverted Exponential Distribution under Records Data: A Comparison Perspective. Mathematics. 10(7). 1047–1047. 1 indexed citations
8.
Wang, Liang, et al.. (2022). Inference and prediction of progressive Type-II censored data from Unit-Generalized Rayleigh distribution. Hacettepe Journal of Mathematics and Statistics. 51(6). 1752–1767. 7 indexed citations
10.
Tripathi, Yogesh Mani, et al.. (2022). On partially observed competing risk model under generalized progressive hybrid censoring for Lomax distribution. Quality Technology & Quantitative Management. 19(5). 562–586. 7 indexed citations
11.
Wang, Liang, et al.. (2021). Statistical Inference of Left Truncated and Right Censored Data from Marshall–Olkin Bivariate Rayleigh Distribution. Mathematics. 9(21). 2703–2703. 5 indexed citations
12.
Nassar, Mazen, Sanku Dey, Liang Wang, & Ahmed Elshahhat. (2021). Estimation of Lindley constant-stress model via product of spacing with Type-II censored accelerated life data. Communications in Statistics - Simulation and Computation. 53(1). 288–314. 22 indexed citations
13.
Wang, Liang, et al.. (2021). Analysis of dependent left-truncated and right-censored competing risks data with partially observed failure causes. Mathematics and Computers in Simulation. 194. 285–307. 5 indexed citations
14.
Wang, Liang, et al.. (2021). Estimation for Weibull Parameters with Generalized Progressive Hybrid Censored Data. Journal of Mathematics. 2021. 1–13. 4 indexed citations
15.
Wang, Liang, et al.. (2020). Reliability analysis for stress-strength model from a general family of truncated distributions under censored data. Communication in Statistics- Theory and Methods. 49(15). 3589–3608. 6 indexed citations
16.
Wang, Liang, et al.. (2020). Inference for dependent competing risks from bivariate Kumaraswamy distribution under generalized progressive hybrid censoring. Communications in Statistics - Simulation and Computation. 51(6). 3100–3123. 7 indexed citations
17.
Wang, Liang, et al.. (2019). Inference for exponential competing risks data under generalized progressive hybrid censoring. Communications in Statistics - Simulation and Computation. 51(3). 1255–1271. 11 indexed citations
18.
Wang, Liang. (2017). Inference of constant-stress accelerated life test for a truncated distribution under progressive censoring. Applied Mathematical Modelling. 44. 743–757. 19 indexed citations
19.
Bera, Anil K., Antonio F. Galvao, & Liang Wang. (2013). On Testing the Equality of Mean and Quantile Effects. 3(1). 47–62. 6 indexed citations
20.
Wang, Liang, et al.. (2011). RELIABILITY ANALYSIS FOR THE TWO-PARAMETER PARETO DISTRIBUTION UNDER RECORD VALUES. Journal of applied mathematics & informatics. 29. 1435–1451. 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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