Results 251 to 260 of about 89,908 (279)

Geographically Weighted Beta Regression

Spatial Statistics, 2017
Abstract Linear regression models are often used to describe the relationship between a dependent variable and a set of independent variables. However, these models are based on the assumption that the error (or, in some cases, the response variable) is normally distributed with constant variance and that the relations are equal throughout space ...
Alan Ricardo da Silva, Andreza de Oliveira Lima   +1 more
openaire   +1 more source

Geographically Weighted Regression

Journal of the American Statistical Association, 2003
(2003). Geographically Weighted Regression. Journal of the American Statistical Association: Vol. 98, No. 463, pp. 765-766.
openaire   +1 more source

Geographically Weighted Regression

2020
The family of geographically weighted regression (GWR) methods has seen its wide applications in a variety of fields including ecology, agriculture, social science, and public health. The popularity of these methods stems from their ability to depict spatial heterogeneity, easy interpretation of outputs, and the availability of user-friendly software ...
openaire   +1 more source

Multiscale Geographically Weighted Regression (MGWR)

Annals of the American Association of Geographers, 2017
Scale is a fundamental geographic concept, and a substantial literature exists discussing the various roles that scale plays in different geographical contexts. Relatively little work exists, though, that provides a means of measuring the geographic scale over which different processes operate. Here we demonstrate how geographically weighted regression
A. Stewart Fotheringham, Wenbai Yang, Wei Kang   +2 more
openaire   +1 more source

Geographically Weighted Negative Binomial Regression—incorporating overdispersion

Statistics and Computing, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
da Silva, Alan Ricardo, Valadares Rodrigues, Thais Carvalho   +1 more
openaire   +1 more source

Geographically-Weighted Regression

2009
A. Páez, D.C. Wheeler
openaire   +2 more sources

Geographically Weighted Regression

2021
Xiang Que, Shaoqiang Su
openaire   +1 more source

Parameter estimation in geographically weighted regression

2009 17th International Conference on Geoinformatics, 2009
Proposed and implemented is a regression framework, which extends the programming language Java with regression analysis, i.e., the capability to do parameter estimation for a function. The regression framework is unique in that functional forms for regression analysis are expressed as Java programs, in which some parameters are not a priori known, but
openaire   +1 more source

Geographically Weighted Regression in Geospatial Analysis

2012
Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial non-stationarity. The assumption in GWR is that observations nearby have a greater influence on parameter estimates than observations at a greater distance.
Rajesh Bahadur Thapa, Ronald C. Estoque
openaire   +1 more source

Stochastic gradient geographical weighted regression (sgGWR): scalable bandwidth optimization for geographically weighted regression

International Journal of Geographical Information Science, 2023
Hayato Nishi, Yasushi Asami
openaire   +1 more source