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Kato's research is in the fields of mathematical statistics, econometrics, and economic statistics with a focus on high dimensional statistical and econometric models.
His research interests include high dimensional statistical inference, causal inference, multiple testing, network clustering, stochastic optimization and resampling methods.
My research interests include high dimensional statistical inference, causal inference, multiple hypothesis testing, optimization, resampling methods, time series analysis and econometrics.
It consists of a novel multi-mounted cone-beam scanning geometry and the corresponding three dimensional statistical iterative reconstruction algorithm.
We developed a high dimensional statistical framework where cluster analysis was used to characterize regional landscape typologies and those typologies are linked to the outcome of interest through regression modeling.
In particular, they proposed a general finite dimensional statistical inference framework based on the moments method in the finite case, which takes a set of moments as input and produces sets of moments as output with the dimensions of the matrices considered finite.
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His research is in high-dimensional statistical learning, a rapidly growing area in statistics that arises from the emergence of the "big data".
ORF 566 High Dimensional Statistics Course is on statistical theory and methods for high-dimensional statistical learning and inferences arising from processing massive data from various scientific disciplines.
Research Interests: High-dimensional statistical learning.
Lu's research explores theoretical and algorithmic aspects of high-dimensional statistical information processing, computational harmonic analysis, imaging and image processing.
In a second step, we adapt various bi-dimensional statistical models to these surfaces of data over age and time.
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CEO of Professional Science Editing for Scientists @ prosciediting.com