Generalised Additive Models

By R. West. 2012

180 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a878388e4118bcd996e1f4194377b8453aa7f152

How researchers see this paper

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

In Artificial Intelligence

1. Andrew Gelman
2. Galin L. Jones
3. Xiao-Li Meng
4. Steve P. Brooks
5. Said Munir
6. Turki M. Habeebullah
7. Arwa S. Sayegh
8. Michael Betancourt

In Management Science and Operations Research

1. Andrew Gelman
2. Said Munir
3. Xiao-Li Meng
4. Galin L. Jones
5. Turki M. Habeebullah
6. Arwa S. Sayegh

In Statistics and Probability

1. Xiao-Li Meng
2. Andrew Gelman
3. Galin L. Jones
4. Steve P. Brooks
5. Said Munir
6. Turki M. Habeebullah
7. Arwa S. Sayegh
8. Paul-Christian Burkner

Citations in the corpus

• Handbook of Markov Chain Monte Carlo: Hardcover: 619 pages Publisher: Chapman and Hall/CRC Press (first edition, May 2011) Language: English ISBN-10: 1420079417 (2012, background): “Here, a curve f is estimated by where z + = max ( 0, z ) and κ i , i = 1, . . . , k , represent the locations of k knot points (Hastie and Tibshirani, 1990).”
• Comparing the Performance of Statistical Models for Predicting PM10 Concentrations (2014, methodology): “GAM is a statistical modelling technique developed by Wahba (1990), and Hastie and Tibshirani (1990) as an extension of the Generalised Linear Models (GLM).”