Su‐Fen Yang

1.2k total citations
74 papers, 1.0k citations indexed

About

Su‐Fen Yang is a scholar working on Statistics, Probability and Uncertainty, Statistics and Probability and Control and Systems Engineering. According to data from OpenAlex, Su‐Fen Yang has authored 74 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Statistics, Probability and Uncertainty, 37 papers in Statistics and Probability and 16 papers in Control and Systems Engineering. Recurrent topics in Su‐Fen Yang's work include Advanced Statistical Process Monitoring (65 papers), Scientific Measurement and Uncertainty Evaluation (34 papers) and Advanced Statistical Methods and Models (32 papers). Su‐Fen Yang is often cited by papers focused on Advanced Statistical Process Monitoring (65 papers), Scientific Measurement and Uncertainty Evaluation (34 papers) and Advanced Statistical Methods and Models (32 papers). Su‐Fen Yang collaborates with scholars based in Taiwan, United States and Canada. Su‐Fen Yang's co-authors include Smiley W. Cheng, Li Yang, Rui Peng, Chi-Guhn Lee, Barry C. Arnold, Ying‐Chao Hung, A.H.M.A. Rahim, Chih‐Ching Yang, Li‐Pang Chen and Tsung‐Chi Cheng and has published in prestigious journals such as European Journal of Operational Research, Expert Systems with Applications and IEEE Access.

In The Last Decade

Su‐Fen Yang

72 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Su‐Fen Yang Taiwan 17 836 485 201 170 123 74 1.0k
Jen Tang United States 15 401 0.5× 348 0.7× 93 0.5× 328 1.9× 106 0.9× 33 772
Loon‐Ching Tang Singapore 14 358 0.4× 350 0.7× 66 0.3× 423 2.5× 84 0.7× 32 774
Muhammad Azam Pakistan 21 1.2k 1.5× 701 1.4× 212 1.1× 85 0.5× 416 3.4× 79 1.4k
Cai Wen Zhang China 12 235 0.3× 111 0.2× 77 0.4× 110 0.6× 65 0.5× 16 452
Bahram Sadeghpour Gildeh Iran 15 476 0.6× 395 0.8× 103 0.5× 53 0.3× 304 2.5× 96 702
Amitava Mukherjee India 23 1.4k 1.7× 954 2.0× 343 1.7× 37 0.2× 216 1.8× 101 1.6k
Jezdimir Knežević United Kingdom 13 149 0.2× 72 0.1× 105 0.5× 324 1.9× 102 0.8× 41 537
Antônio Fernando Branco Costa Brazil 30 2.5k 3.0× 1.2k 2.5× 534 2.7× 50 0.3× 398 3.2× 114 2.6k
Roberto da Costa Quinino Brazil 13 301 0.4× 175 0.4× 49 0.2× 31 0.2× 112 0.9× 61 471

Countries citing papers authored by Su‐Fen Yang

Since Specialization
Citations

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

Fields of papers citing papers by Su‐Fen Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Su‐Fen Yang. 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 Su‐Fen Yang. The network helps show where Su‐Fen Yang may publish in the future.

Co-authorship network of co-authors of Su‐Fen Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Su‐Fen Yang. A scholar is included among the top collaborators of Su‐Fen Yang 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 Su‐Fen Yang. Su‐Fen Yang 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.
Yang, Su‐Fen, et al.. (2024). Pollution concentration monitoring using a new Birnbaum–Saunders control chart. Quality and Reliability Engineering International. 40(7). 3913–3933.
2.
Yang, Su‐Fen, et al.. (2024). Re‐Unveiling the energy efficiency impact: Paving the way for sustainable growth in ASEAN countries. Sustainable Development. 32(5). 5812–5824. 4 indexed citations
3.
Yang, Su‐Fen, et al.. (2023). Dynamic and Non-Linear Analysis of the Impact of Diurnal Temperature Range on Road Traffic Accidents. Climate. 11(10). 199–199. 2 indexed citations
4.
Yang, Su‐Fen, et al.. (2023). A New EWMA Control Chart for Monitoring Multinomial Proportions. Sustainability. 15(15). 11797–11797. 2 indexed citations
5.
Yang, Su‐Fen, et al.. (2023). A phase II multivariate EWMA chart for monitoring multi-dimensional ratios of process means with individual observations. Computers & Industrial Engineering. 183. 109490–109490. 4 indexed citations
6.
Yang, Su‐Fen, Li‐Pang Chen, & Cheng‐Kuan Lin. (2023). Adjustment of Measurement Error Effects on Dispersion Control Chart with Distribution-Free Quality Variable. Sustainability. 15(5). 4337–4337. 3 indexed citations
7.
Yang, Su‐Fen, et al.. (2023). The Non-Linear Relationship between Air Pollution, Labor Insurance and Productivity: Multivariate Adaptive Regression Splines Approach. Sustainability. 15(12). 9404–9404. 5 indexed citations
8.
Yang, Su‐Fen, et al.. (2022). Logarithmic confidence estimation of a ratio of binomial proportions for dependent populations. Journal of Applied Statistics. 50(8). 1750–1771. 2 indexed citations
9.
Yang, Su‐Fen, et al.. (2021). Logarithmic confidence intervals for the cross-product ratio of binomial proportions under different sampling schemes. Communications in Statistics - Simulation and Computation. 52(6). 2686–2704. 2 indexed citations
10.
Yang, Su‐Fen, et al.. (2017). A median loss control chart for monitoring quality loss under skewed distributions. Journal of Statistical Computation and Simulation. 87(17). 3241–3260. 7 indexed citations
11.
Yang, Su‐Fen. (2013). Using a new VSI EWMA average loss control chart to monitor changes in the difference between the process mean and target and/or the process variability. Applied Mathematical Modelling. 37(16-17). 7973–7982. 29 indexed citations
12.
Yang, Su‐Fen. (2013). Using a Single Average Loss Control Chart to Monitor Process Mean and Variability. Communications in Statistics - Simulation and Computation. 42(7). 1549–1562. 12 indexed citations
13.
Hung, Ying‐Chao, et al.. (2012). A framework for nonparametric profile monitoring. Computers & Industrial Engineering. 64(1). 482–491. 34 indexed citations
14.
Yang, Su‐Fen, et al.. (2010). Using VSI Loss Control Charts to Monitor a Process with Incorrect Adjustment. Communications in Statistics - Simulation and Computation. 39(4). 736–749. 8 indexed citations
15.
Yang, Su‐Fen, et al.. (2010). Monitoring and diagnosing dependent process steps using VSI control charts. Journal of Statistical Planning and Inference. 141(5). 1808–1816. 16 indexed citations
16.
Yang, Su‐Fen, et al.. (2008). Monitoring and Diagnosing Multistage Processes: A Review of Cause Selecting Control Charts. Journal of industrial and systems engineering.. 2(3). 214–235. 28 indexed citations
17.
Yang, Su‐Fen, et al.. (2006). Adaptive control schemes for two dependent process steps. Journal of Loss Prevention in the Process Industries. 20(1). 15–25. 10 indexed citations
18.
Yang, Su‐Fen, et al.. (2005). An approach to controlling two dependent process steps with autocorrelated observations. The International Journal of Advanced Manufacturing Technology. 29(1-2). 170–177. 20 indexed citations
19.
Yang, Su‐Fen. (2003). Optimal Processes Control for a Failure Mechanism. Communications in Statistics - Simulation and Computation. 32(4). 1285–1314. 9 indexed citations
20.
Yang, Su‐Fen & A.H.M.A. Rahim. (2000). Economic statistical design for and S2control charts: A markov chain approach. Communications in Statistics - Simulation and Computation. 29(3). 845–873. 15 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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