Choosing the number of factors in partial least squares regression: estimating and minimizing the mean squared error of prediction
By M. C. Denham. 2000
69 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Food Science, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/6d434aecd862aa19542bbaa5608230c2a92ed7e8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Analytical Chemistry
- Lennart Eriksson
- Lucka Kajfez-Bogataj
- Svante Wold
- Klemen Bergant
- Johan Trygg
- Robert Powers
- Bradley Worley
- David J. Biagioni
In Food Science
In Statistics and Probability
- Lucka Kajfez-Bogataj
- Svante Wold
- Michael Sjöström
- David Nilsson
- S. Wiklund
- K. Faber
- Klemen Bergant
- Lennart Eriksson
Citations in the corpus
- A randomization test for PLS component selection (2007, background): “In 1996, Denham [2] noted that ‘Unfortunately, the statistical behavior of PLS regression is still not well understood and this has meant that it has been extremely difficult to perform the usual inferential tasks associatedwith modeling, such as choosing an optimal model, assessing uncertainty in c”
- N–PLS regression as empirical downscaling tool in climate change studies (2005, methodology): “In general, this method is suggested for cases with small numbers of samples (Wilks, 1995), and was found to be successful in the case of PLS regression models, especially when the predictors are strongly correlated (Denham, 2000).”