Work

Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions

Year: 2019

Type: article

Source: Spatial Statistics

DOI: 10.1016/j.spasta.2019.02.003

Authors: Daisuke Murakami, Daniel A. Griffith

Institutions: The Institute of Statistical Mathematics, The University of Texas at Dallas

Language: English

Topic: Spatial and Panel Data Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

Funder: Japan Society for the Promotion of Science

Awards: Extension of eigenvectir spatial filtering approaches for large and diverse spatiotemporal datasets, Challenges and new developments to the most important issues in spatial econometrics, Development of Methodology for Geographic Crime Prediction -Establishment of academic infrastructure and system development through interdisciplinary research and industry-academia collaboration

OpenAlex ID: W2883124148

FWCI: 7.082

Cites: 79

Cited by: 48

Related to: 10