15 citations · 17 across the 5 of their papers we have counts for
5 papers
Fast spatio-temporally varying coefficient modeling with reluctant interaction selection
Daisuke Murakami, Shinichiro Shirota, Seiji Kajita +1
Spatially and temporally varying coefficient (STVC) models are currently attracting attention as a flexible tool to explore the spatio-temporal patterns in regression coefficients.…
Spatial regression-based transfer learning for prediction problems
Daisuke Murakami, Mami Kajita, Seiji Kajita
Although spatial prediction is widely used for urban and environmental monitoring, its accuracy is often unsatisfactory if only a small number of samples are available in the study…
Compositionally-warped additive mixed modeling for a wide variety of non-Gaussian spatial data
Daisuke Murakami, Mami Kajita, Seiji Kajita +1
As with the advancement of geographical information systems, non-Gaussian spatial data sets are getting larger and more diverse. This study develops a general framework for fast an…
Scalable model selection for spatial additive mixed modeling: application to crime analysis
Daisuke Murakami, Mami Kajita, Seiji Kajita
A rapid growth in spatial open datasets has led to a huge demand for regression approaches accommodating spatial and non-spatial effects in big data. Regression model selection is…
Crime Prediction by Data-Driven Green's Function method
Mami Kajita, Seiji Kajita
We develop an algorithm that forecasts cascading events, by employing a Green's function scheme on the basis of the self-exciting point process model. This method is applied to ope…