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Statistical Analysis and Sparse Estimation Lab
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A lab researching statistical analysis methods for extracting useful information from large-scale data.
AboutAbout the lab
Extracting information from data with new statistical methods
Kei Hirose's lab conducts research and development on new statistical analysis methods, based on mathematical statistics.
Analyzing large-scale data such as genetic data requires statistical models with tens of thousands to hundreds of millions of parameters.
The lab develops new sparse estimation methods, which extract useful information by estimating most of a huge number of parameters as zero, within multivariate analysis such as factor analysis and graphical models.
The lab's website introduces it under the name "Kei Hirose Lab."
Key terms
- Sparse estimation
- A method that extracts useful information by estimating most of a large number of parameters as zero.
- Factor analysis
- A method for capturing a small number of latent factors behind many observed variables.
- Latent factor
- A small number of factors assumed to lie behind the many variables that are observed.
NewsLab news
View More ↗ResearchResearch themes
The lab conducts research and development on new statistical analysis methods based on mathematical statistics. It also incorporates the ideas behind methods used in machine learning, and applications to real-world data analysis are among its research themes.
*Illustrative imageTheory and estimation methods for factor analysis
The lab studies the theory, estimation methods, rotation methods, and interpretability of factor analysis, which captures a small number of latent factors behind many observed variables. It is a method used in a wide range of fields, including psychology and the social sciences.
*Illustrative imageSparse estimation for high-dimensional data
In the analysis of high-dimensional data, sparse estimation, which estimates parameters while automatically selecting the important variables, becomes essential. The lab studies its theory, computational methods, and applications.
*Illustrative imageStatistical analysis of real data
This research applies statistical methods to real-world data analysis. The lab performs statistical data analysis on real data, such as electricity demand forecasting and the prediction of material properties.
MembersFaculty
- ProfessorKei Hirose
VoicesStudent voices
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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: “Factor rotatie” Frederik Beuk / Public domain (cropped)
- Theory and estimation methods for factor analysis: “Questionnaire-checklist-completed” https://pixabay.com/users/ / / CC0 (cropped)
- Sparse estimation for high-dimensional data: “3-dimensional series of spheres LCCN” Miscellaneous Items in High Demand, PPOC, Library of Congress / Public domain (cropped)
- Statistical analysis of real data: “01fig-inference” Loneshieling / CC BY-SA 4.0 (cropped)
- Related lab (Statistical Analysis and Sparse Estimation Lab (Kei Hirose)): “Data Analysis ( )” IAEA Imagebank / CC BY 2.0 (cropped)
- Related lab (〈Statistical Science / Small Area Estimation〉): “Colorado census statistical areas 2020” Buaidh / CC0 (cropped)
- Related lab (Statistical Science and Small Area Estimation Lab (Masayo Hirose)): “1922 Index of Great Trigonometrical Survey of India” Survey of India, Government of India / Public domain (cropped)
- Related lab (〈Bayesian Statistics / Uncertainty Quantification〉): “VLT 4LGSF OTA L2 lens measurement on NANOMEFOS (tno kamphues 22)” F. Kamphues/TNO/ESO / CC BY 4.0 (cropped)
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