63 citations · 79 across the 8 of their papers we have counts for
7 papers · 1 filter
Some Simplifications for the Expectation-Maximization (EM) Algorithm: The Linear Regression Model Case
Daniel A. Griffith
The EM algorithm is a generic tool that offers maximum likelihood solutions when datasets are incomplete with data values missing at random or completely at random. At least for it…
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…
Low rank spatial econometric models
Daisuke Murakami, Hajime Seya, Daniel A. Griffith
This article presents a re-structuring of spatial econometric models in a linear mixed model framework. To that end, it proposes low rank spatial econometric models that are robust…
Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions
Daisuke Murakami, Daniel A. Griffith
While spatially varying coefficient (SVC) modeling is popular in applied science, its computational burden is substantial. This is especially true if a multiscale property of SVC i…
The importance of scale in spatially varying coefficient modeling
Daisuke Murakami, Binbin Lu, Paul Harris +4
While spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study…
Eigenvector spatial filtering for large data sets: fixed and random effects approaches
Daisuke Murakami, Daniel A. Griffith
Eigenvector spatial filtering (ESF) is a spatial modeling approach, which has been applied in urban and regional studies, ecological studies, and so on. However, it is computationa…
A Moran coefficient-based mixed effects approach to investigate spatially varying relationships
Daisuke Murakami, Takahiro Yoshida, Hajime Seya +2
This study develops a spatially varying coefficient model by extending the random effects eigenvector spatial filtering model. The developed model has the following properties: its…