Spatial spline regression models

By Laura M. Sangalli, James O. Ramsay, T. Ramsay. 2013

177 citations recorded by the source corpus.

Subfields: Environmental Engineering, Nature and Landscape Conservation, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/61028d71704d7cc15537dcca729f50fb62cfbc88

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. B. Ettinger
2. Simona Perotto
3. Li Wang
4. Laura M. Sangalli
5. Jingru Mu
6. Lijian Yang
7. Guannan Wang

In Nature and Landscape Conservation

1. B. Ettinger
2. Simona Perotto
3. Laura M. Sangalli

In Statistics and Probability

1. Li Wang
2. Lijian Yang
3. Guannan Wang
4. Shan Yu
5. Chenhui Liu
6. Jingru Mu
7. B. Ettinger
8. Simona Perotto

Citations in the corpus

• Estimation and inference in spatially varying coefficient models (2018, background): “…suffer from the problem of “leakage” across the complex domains, which refers to the poor estimation over difficult regions by smoothing inappropriately across boundary features; see the discussions by Ramsay (2002); Sangalli, Ramsay, and Ramsay (2013); and Wood, Bravington, and Hedley (2008).”
• Spatial regression models over two-dimensional manifolds (2016, methodology): “In particular, we compare three methods: the proposed Spatial Regression models over Non-Planar domains1 (SR-NP), the Spatial Regression models over Planar domains (SR-P) introduced in [22] and [23], and Iterative Heat Kernel smoothing (IHK) described in [6].”
• Estimation and Inference for Generalized Geoadditive Models (2020, background): “It might be attributed to that males travel more than females for business and work (Collins and Tisdell 2002), and they are also more likely to drive aggressively than female (Shinar and Compton 2004).”