1 citations · 1 across the 2 of their papers we have counts for
3 papers
stat.AP2022
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…
stat.ME2021
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…
stat.AP2020★ 1 cited
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…