Paul Harris
Economics and Econometrics · 2010–2014
A researcher in the Science corpus: 6 papers; trusts 28 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Economics and Econometrics — 48 citations into it; #8,740 of 23,481 by trust here, #11,786 by citations
- Statistics and Probability — 29 citations into it
- Environmental Engineering — 23 citations into it; #4,204 of 5,479 by trust here, #4,892 by citations
- Global and Planetary Change — 17 citations into it
- Artificial Intelligence — 11 citations into it; #29,405 of 35,345 by trust here, #27,551 by citations
- Applied Mathematics — 7 citations into it
- Transportation — 6 citations into it; #972 of 2,062 by trust here, #1,673 by citations
- Building and Construction — 5 citations into it
Whom Paul Harris trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David C. Wheeler
- Edzer J. Pebesma
- Christopher D. Lloyd
- Montserrat Fuentes
- Jorge Kazuo Yamamoto
- Chris Brunsdon
- Christopher J. Paciorek
- William S. Cleveland
- Pierre Goovaerts
- Adrian W. Bowman
Who trusts Paul Harris overall
- Isabella Gollini
- Binbin Lu
- Francky Fouedjio
- Jin Li
- Martin Charlton
- Chris Brunsdon
- Andrew D. Heap
- Rui Zhao
- Qinxue Meng
Papers from Paul Harris's perspective
Ordered by how strongly the researchers Paul Harris trusts endorse each paper. Same corpus, a different order for every researcher.
- Geostatistics for Natural Resources Evaluation
- Simultaneous Coefficient Penalization and Model Selection in Geographically Weighted Regression: The Geographically Weighted Lasso
- Diagnostic Tools and a Remedial Method for Collinearity in Geographically Weighted Regression
- Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model
- Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor
- Robust Locally Weighted Regression and Smoothing Scatterplots
- A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 1. Location-Specific Kernel Bandwidths and a Test for Locational Heterogeneity
- Multivariable geostatistics in S: the gstat package (Computational Geosciences)
- Multicollinearity and correlation among local regression coefficients in geographically weighted regression (Journal of Geographical Systems)
- Can Geographically Weighted Regressions Improve Regional Analysis and Policy Making?
Their papers
- GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models (2013) — 582 citations
- Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor (2011) — 60 citations
- Moving window kriging with geographically weighted variograms (2010) — 62 citations
- Principal Component Analysis on Spatial Data: An Overview (2013) — 453 citations
- Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data (2014) — 375 citations
- Living with Collinearity in Local Regression Models (2012) — 47 citations