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20172022
most citedThe GWR route map: a guide to the informed application of Geographically Weighted Regression

36 citations · 44 across the 8 of their papers we have counts for

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10 papers · 1 filter

stat.ME2021

Analysis of COVID-19 evolution based on testing closeness of sequential data

Tomoko Matsui, Nourddine Azzaoui, Daisuke Murakami

A practical algorithm has been developed for closeness analysis of sequential data that combines closeness testing with algorithms based on the Markov chain tester. It was applied…

stat.ME20211 cited

Adaptively Robust Geographically Weighted Regression

Shonosuke Sugasawa, Daisuke Murakami

We develop a new robust geographically weighted regression method in the presence of outliers. We embed the standard geographically weighted regression in robust objective function…

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.ME2020

Spatially Clustered Regression

Shonosuke Sugasawa, Daisuke Murakami

Spatial regression or geographically weighted regression models have been widely adopted to capture the effects of auxiliary information on a response variable of interest over a r…

stat.ME202036 cited

The GWR route map: a guide to the informed application of Geographically Weighted Regression

Alexis Comber, Chris Brunsdon, Martin Charlton +8

Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships…

stat.ME2019

A memory-free spatial additive mixed modeling for big spatial data

Daisuke Murakami, Daniel A. Griffith

This study develops a spatial additive mixed modeling (AMM) approach estimating spatial and non-spatial effects from large samples, such as millions of observations. Although fast…