63 citations · 79 across the 8 of their papers we have counts for
8 papers
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…
Balancing spatial and non-spatial variation in varying coefficient modeling: a remedy for spurious correlation
Daisuke Murakami, Daniel A. Griffith
This study discusses the importance of balancing spatial and non-spatial variation in spatial regression modeling. Unlike spatially varying coefficients (SVC) modeling, which is po…
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…