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Murakami Lab
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A lab that studies nonparametric methods, statistical techniques that do not rely on assumptions about the distribution.
AboutAbout the lab
Statistics and nonparametric methods.
The Murakami Lab studies statistics and nonparametric methods.
Nonparametric methods are highly versatile statistical techniques that do not require assumptions about the population distribution.
The lab also works on statistical process control, approximate distributions, and multivariate analysis.
In 2008, Prof. Murakami started a statistics study group in which students, young researchers, and faculty members discuss statistics across the boundaries of universities and research fields.
Key terms
- Nonparametric methods
- Highly versatile statistical techniques that do not require assumptions about the population distribution
- Statistical process control
- A field that deals with the problem of detecting outliers. Accurate detection is important in meteorology, economics, quality control, and other areas
- Saddlepoint approximation
- One of the theories for approximating distribution functions
ResearchResearch themes
Within statistics, the lab works on nonparametric methods, which do not require assumptions about the population distribution. Its main research areas are nonparametric methods, statistical process control, approximation methods, and multivariate analysis.
Nonparametric methods
The lab works on highly versatile statistical techniques that do not require assumptions about the population distribution. They are used in a wide range of fields, including those where collecting data is difficult, such as medicine, pharmacy, and ecology.
*Illustrative imageOutlier detection (statistical process control)
Detecting outliers accurately is important in many fields, including meteorology, economics, and quality control. The lab applies nonparametric test statistics to the problem of outlier detection.
*Illustrative imageDistribution approximation theory
In statistics, distribution functions play an extremely important role. The lab studies a variety of approximation theories, including saddlepoint approximation.
*Illustrative imageMultivariate analysis
Statistical techniques for data composed of multiple variables. They analyze many variables at once to reveal the structure of the data.
MembersFaculty
- Hidetoshi Murakami
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: “Inspektor India membuat pemeriksaan QC menggunakan mesin AXI” Encik Tekateki / CC BY-SA 4.0 (cropped)
- Outlier detection (statistical process control): “002 Production line - car assembly line in General Motors Manufacturing Poland - Gliwice, Poland” Marek Ślusarczyk (Tupungato) Photo portfolio / CC BY 3.0 (cropped)
- Distribution approximation theory: “Rechenhilfsmittel (historisch)” Rhetos / CC0 (cropped)
- Multivariate analysis: “Spire 24h AIS Archive over Gulf of Oman” Quentin Gollier / CC BY-SA 4.0 (cropped)
- Related lab (瀬尾研究室): “Mathematician analyzing data. - DPLA - 60af5ced0800f6f7d6c99b5587a9ae0e” Peterson, Jon / Public domain (cropped)
- Related lab (中山研究室): “Răstoliţa, train schedule” cs:ŠJů / CC BY-SA 3.0 (cropped)
- Related lab (橋口研究室): “Random-pixels-black-white” Georg-Johann / CC0 (cropped)
- Related lab (石渡研究室): “Star Cluster R )” Credit: NASA, ESA, and STScI / Public domain (cropped)
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