Taylor M. Oshan
Economics and Econometrics · 2018–2019
A researcher in the Science corpus: 4 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Economics and Econometrics — 14 citations into it
- Statistics and Probability — 13 citations into it
Whom Taylor M. Oshan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Daniel A. Griffith
- David C. Wheeler
- Robert M. O’Brien
- A. Stewart Fotheringham
- David Wheeler
- Michael Tiefelsdorf
Papers from Taylor M. Oshan's perspective
Ordered by how strongly the researchers Taylor M. Oshan trusts endorse each paper. Same corpus, a different order for every researcher.
- Multicollinearity and correlation among local regression coefficients in geographically weighted regression (Journal of Geographical Systems)
- A Comparison of Spatially Varying Regression Coefficient Estimates Using Geographically Weighted and Spatial‐Filter‐Based Techniques
- Inference in Multiscale Geographically Weighted Regression (Geographical Analysis)
- 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
- Spatial Modeling With Spatially Varying Coefficient Processes (Journal of the American Statistical Association)
- Spatial-Filtering-Based Contributions to a Critique of Geographically Weighted Regression (GWR)
- Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model
- Spatial Autocorrelation and Spatial Filtering
- Hierarchical Modeling and Analysis for Spatial Data
Their papers
- Inference in Multiscale Geographically Weighted Regression (2019) — 371 citations
- A Comparison of Spatially Varying Regression Coefficient Estimates Using Geographically Weighted and Spatial‐Filter‐Based Techniques (2018) — 69 citations
- On the measurement of bias in geographically weighted regression models (2019) — 58 citations
- mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale (2018) — 703 citations