T. Lwin

2.0k citations
46 papers · 1.5k · h-index 16

Impact in

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

Papers in

T. Lwin

46 papers receiving 1.4k citations

Peers

T. Lwin
Comparison fields: 5 of 142
  • Statistics and Probability 420
  • Electrochemistry 265
  • Statistics, Probability and Uncertainty 226
  • Catalysis 61
  • Materials Chemistry 336
Replace Fritz Scholz with:
Fritz Scholz Germany
I. I. Berenblut United Kingdom
D. W. Bacon Canada
Sajjad Ahmad Khan Pakistan
Chiwoo Park United States
David Cox United Kingdom
Liqun Wang China
Da Chen China
F. S. Lai United States
T. Lwin relative to Fritz Scholz Germany Fritz Scholz's profile →
Citations per field
00.5×5.4×
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 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1983335
2 1991177
3 2001164
4 1990145
5 2002113
6 1990104
7 199079
8 197248
9 198447
10 198240
11 200933
12 199929
13 201825
14 198325
15 198024
16 200423
17 198915
18 198713
19 19747
20 19946

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 46 papers that have together received 1.5k 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), Fault Detection and Control Systems (5 papers), Soil Geostatistics and Mapping (5 papers), Statistical Distribution Estimation and Applications (5 papers), Reliability and Maintenance Optimization (3 papers) and Statistical Methods and Inference (3 papers). The work is most often cited by research in Statistics and Probability (420 citations), Electrochemistry (265 citations), Statistics, Probability and Uncertainty (226 citations), Catalysis (61 citations) and Materials Chemistry (336 citations). T. Lwin has collaborated with scholars based in Australia, United Kingdom and Myanmar. Frequent co-authors include J. S. Maritz, Stephen Fletcher, Cameron Halliday, Gill Nelson, D. J. Gates, Mark Westcott, Nicola V. Y. Scarlett, L. M. D. Cranswick, Ian C. Madsen and D. V. Lindley. Their work appears in journals such as Scandinavian Actuarial Journal, Journal of the Royal Statistical Society Series C (Applied Statistics), International Journal of Mineral Processing, Technometrics 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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