Statistical and numerical algorithms
Statistical and numerical algorithms is a research topic within Applied Mathematics. Science Explorer counts 12k research works in it since 1950. 14.9% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the theory, algorithms, and applications of Total Least Squares (TLS) methods, including Singular Spectrum Analysis, weighted least squares, geodetic transformations, forecasting models, errors-in-variables models, structured low-rank approximation, parameter estimation, and time series analysis.
- Total Least Squares
- Singular Spectrum Analysis
- Weighted Least Squares
- Geodetic Transformations
- Forecasting
- Errors-in-Variables Models
- Structured Low-Rank Approximation
- Parameter Estimation
- Time Series Analysis
- Regression
- Research works
- 12k fractional, since 1950
- In the world top 10%
- 1.9k per year above
- Top-10% rate
- 14.9% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +8% the tick is no change
Which countries lead Statistical and numerical algorithms research?
By volume, China and the United States publish the most (245 and 242 works in 2022–2025).
By volume, 2022–2025
- 1 China 245 works
- 2 United States 242 works
- 3 Russia 159 works
- 4 Germany 73 works
- 5 India 61 works
- 6 France 57 works
- 7 United Kingdom 52 works
- 8 Italy 50 works
- 9 Japan 40 works
- 10 Indonesia 39 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Statistical and numerical algorithms research?
By volume in 2022–2025, St Petersburg University publishes the most Statistical and numerical algorithms research, followed by Lomonosov Moscow State University and Tongji University.
By volume, 2022–2025
- 1 St Petersburg UniversityRussia 9 works
- 2 Lomonosov Moscow State UniversityRussia 8 works
- 3 Tongji UniversityChina 8 works
- 4 Sapienza University of RomeItaly 7 works
- 5 Moscow Aviation InstituteRussia 7 works
- 6 Centre National de la Recherche ScientifiqueFrance 7 works
- 7 Wuhan UniversityChina 6 works
- 8 Charles UniversityCzechia 5 works
- 9 Taras Shevchenko National University of KyivUkraine 5 works
- 10 Columbia UniversityUnited States 4 works
Where is Statistical and numerical algorithms research done?
The largest centres of Statistical and numerical algorithms research in 2022–2025 are Moscow (Russia), Beijing (China), Shanghai (China) and Saint Petersburg (Russia). Among places with at least 20 works in it, it is an unusually large share of all research in Saint Petersburg, Kyiv and Moscow.
Largest cities, 2022–2025
Where it is the local speciality
- Saint PetersburgRU · 24.0 works5.9×
- KyivUA · 20.0 works4.7×
- MoscowRU · 63.4 works4.1×
Location quotient: how much more of its research is in Statistical and numerical algorithms than the world average.
Where is the best place to study Statistical and numerical algorithms?
Among universities, judged by research, Lomonosov Moscow State University and St Petersburg University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Lomonosov Moscow State UniversityRussia | 55.0 | 22.8% | 6.6× | 8 | +81.1% |
| 2 | St Petersburg UniversityRussia | 45.0 | 11.4% | 12.4× | 9 | -5.0% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Statistical and numerical algorithms research growing?
Output in 2018–2022 was 8% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Statistical and numerical algorithms.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.