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Mathematical Statistics Lab
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A research and education field that clarifies, through theory, the properties of statistical methods for data analysis.
AboutAbout the lab
Clarifying the properties of data analysis methods through theory.
The field of mathematical statistics conducts research that theoretically clarifies the properties of the statistical and computational methods needed to analyze various kinds of data.
It proposes new data analysis methods, verifies their effectiveness, and also conducts applied research in various sciences.
The research keywords of the faculty members include mathematical statistics, statistical science, computational statistics, sparse estimation, multivariate analysis, Bayesian estimation, and model selection.
Key terms
- Random partition
- The probability distribution that arises when the way a positive integer is split into a sum of positive integers occurs at random.
- Regression analysis
- An analysis method used to describe the relationship between a response variable and explanatory variables.
- Outlier
- A data point whose value lies far from the rest of the data. It is difficult to give a clear definition or dividing line.
ResearchResearch themes
The field of mathematical statistics conducts research that theoretically clarifies the properties of the statistical and computational methods needed to analyze various kinds of data. It proposes new data analysis methods, verifies their effectiveness, and also conducts applied research in various sciences.
*Illustrative imageRandom Partitions and Regression Analysis
A probability distribution over randomly occurring partitions of integers is called a random partition. Associate Professor Koji Tsukuda mainly studies the properties of statistical models of random partitions and inference for them. Research on regression analysis and on statistical methods used in manufacturing is also of interest.
Outlier-Robust Model Selection
Real-world data inevitably contain outliers, and robust methods that keep their influence small are considered important in analysis. Assistant Professor Sumito Kurata studies robust model selection using statistical divergences, which measure how far apart probability distributions are.
MembersFaculty
- ProfessorShuichi Kawano
- ProfessorNguyen Duy Hien
- ProfessorSatoshi Hirose
- Associate ProfessorShingo Saito
- Associate ProfessorKoji Tsukuda
- Associate ProfessorSatoru Tokuda
- Associate ProfessorMasayo Hirose
- Associate ProfessorHiroshi Yoshida
- Assistant ProfessorSumito Kurata
- Assistant ProfessorYuichi Goto
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: “08fig-empirical” ASpiegler / CC BY-SA 4.0 (cropped)
- Random Partitions and Regression Analysis: “M44-Star-Cluster” Chuck Ayoub / CC0 (cropped)
- Related lab (Mathematical Biology Lab): “SaintpauliaLeaf-7” Kārlis Kalviškis / CC BY-SA 4.0 (cropped)
- Related lab (Differential Geometry Lab): “Schwarz H Surface” Anders Sandberg / CC BY-SA 3.0 (cropped)
- Related lab (Mathematical Physics Lab): “Crabbers eye knot-removebg-preview” Zaripov999 / CC BY-SA 4.0 (cropped)
- Related lab (Dynamical Systems Lab): “Lorenz caos3” António Miguel de Campos / Public domain (cropped)
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