T. Lwin

2.0k citations
47 papers · 1.6k · h-index 16

Impact in

    • Statistical Distribution Estimation and Applications
    • Statistical Methods and Bayesian Inference
    • Advanced Statistical Methods and Models
    • Electrochemical Analysis and Applications

Papers in

T. Lwin

46 papers receiving 1.5k citations

Peers

T. Lwin
Comparison fields: 5 of 141
  • Statistics and Probability 429
  • Electrochemistry 271
  • Statistics, Probability and Uncertainty 234
  • Catalysis 62
  • Materials Chemistry 347
Replace Fritz Scholz with:
Fritz Scholz Germany
I. I. Berenblut United Kingdom
D. W. Bacon Canada
Chiwoo Park United States
Da Chen China
David Cox United Kingdom
Luis H. García‐Rubio United States
Liqun Wang China
F. S. Lai United States
T. Lwin relative to Fritz Scholz Germany Fritz Scholz's profile →
Citations per field
00.5×2×3×4×5.0×
Fritz Scholz · 1×
Citations per year

Countries citing papers authored by T. Lwin

Since Specialization
Citations

This map shows the geographic impact of T. Lwin'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 T. Lwin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Lwin more than expected).

Fields of papers citing papers by T. Lwin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1983339
2 1991186
3 2001173
4 1990157
5 2002118
6 1990107
7 199083
8 197252
9 198448
10 200943
11 198241
12 199930
13 201829
14 198027
15 198325
16 200424
17 198915
18 198714
19 19747
20 19867

About T. Lwin

T. Lwin is a scholar working on Statistics and Probability, Artificial Intelligence, Statistics, Probability and Uncertainty, Mechanical Engineering and Control and Systems Engineering, having authored 47 papers that have together received 1.6k indexed citations. Recurring topics across this work include Advanced Statistical Process Monitoring (7 papers), Advanced Statistical Methods and Models (7 papers), Bayesian Methods and Mixture Models (6 papers), Statistical Distribution Estimation and Applications (5 papers), Fault Detection and Control Systems (5 papers), Soil Geostatistics and Mapping (5 papers), Probability and Risk Models (3 papers) and Statistical Methods and Inference (3 papers). The work is most often cited by research in Statistics and Probability (429 citations), Electrochemistry (271 citations), Statistics, Probability and Uncertainty (234 citations), Catalysis (62 citations) and Materials Chemistry (347 citations). T. Lwin has collaborated with scholars based in Australia, United Kingdom and United States. Frequent co-authors include J. S. Maritz, Stephen Fletcher, Ian C. Madsen, L. M. D. Cranswick, Nicola V. Y. Scarlett, D. J. Gates, Cameron Halliday, Gill Nelson, Mark Westcott and D. V. Lindley. Their work appears in journals such as Technometrics, International Journal of Mineral Processing, Journal of the Royal Statistical Society Series C (Applied Statistics), Scandinavian Actuarial Journal and Microelectronics Reliability.

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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