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Uchida Lab
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The lab studies how to infer stochastic differential equation models from high-frequency data.
AboutAbout the lab
Reading noisy data through mathematical models.
Uchida Lab works in mathematical statistics, studying the statistical modeling of stochastic differential equations based on high-frequency time series data.
The lab develops asymptotic theory for model estimation and hypothesis testing, and works on information criteria for model selection together with their mathematical justification.
The lab also studies statistical asymptotic theory for stochastic partial differential equations using high-frequency spatio-temporal data.
The lab is also interested in empirical research using data analysis, and aims to advance statistical modeling from both theoretical and applied perspectives.
Key terms
- Stochastic differential equation
- A differential equation that describes change containing random fluctuations (noise)
- Asymptotic theory
- A theory that examines how the properties of estimation and testing behave as the amount of data grows larger
- Information criterion
- An index that serves as a guide for choosing, from among several statistical models, the one that best fits the data
ResearchResearch themes
The lab studies statistical inference for stochastic differential equations and stochastic partial differential equations based on high-frequency time series and spatio-temporal data. It works on asymptotic theory for estimation and testing, and on information criteria for model selection.
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MembersFaculty
- ProfessorMasayuki UchidaPublications ↗
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Ornstein-uhlenbeck simulation” Loxley / CC BY-SA 4.0 (cropped)
- Statistical Inference for Stochastic Differential Equations: “Wiener process 3d” Sullivan.t.j / CC BY-SA 3.0 (cropped)
- Related lab (Koike Lab): “Tickers ( )” Francisco Gonzalez / CC BY 2.0 (cropped)
- Related lab (Masuda Lab): “NIG-process” Thomas Steiner and Martin Keller-Ressel / CC BY-SA 2.5 (cropped)
- Related lab (Aida Lab): “BMonSphere” Thomas Steiner / CC BY-SA 2.5 (cropped)
- Related lab (Okada Lab): “2D Random Walk Trace” Purpy Pupple / CC BY-SA 3.0 (cropped)
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