Partial least squares regression as an alternative to current regression methods used in ecology

By L. M. Carrascal, I. Galván, Oscar Gordo. 2009

739 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9e25b90ebe5d44a68dcc5aa84de48928928f9718

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

In Environmental Engineering

In Statistics and Probability

Citations in the corpus

Differential response of continental stock complexes of Atlantic salmon (Salmo salar) to the Atlantic Multidecadal Oscillation (2014, methodology): “Following Carrascal et al. (2009), we assessed the significance of each PLSR component for each stock by sequentially regressing the residuals from the previous component against the predicted values for each new component.”
Chronic exposure to low-dose radiation at Chernobyl favours adaptation to oxidative stress in birds (2014, methodology): “However, this method is extremely resilient to the effects of sample size and degree of correlation between predictor variables, which makes PLSR especially useful when the sample size is small and in cases of severe multicollinearity (Carrascal et al. 2009).”
Ontario freshwater fishes demonstrate differing range‐boundary shifts in a warming climate (2014, background): “PLSR is well suited for analysing relationships when the number of predictor variables is higher than the number of observations and when predictor variables are correlated (Carrascal et al., 2009), conditions that are common within ecological data, including this study and are problematic in tradit”
Long-term climate-associated dynamics of a tropical seagrass meadow: implications for the future (2011, methodology): “PLS regression is particularly suited to incidences when the matrix of predictors has more variables than observations, and when there is multicollinearity among variables (Carrascal et al. 2009).”
Using species distribution models in paleobiogeography: A matter of data, predictors and concepts (2011, background): “These techniques can be divided into: (i) those based on the statistical fitting of data to the theoretical relationships between the species' presence and the environment (e.g. GLMz, GAMs, PLS, CART, andMaxEnt; Austin et al., 1990; Yee and Mitchell, 1991; Austin, 2002; Vaz et al., 2008; Carrascal e”
Physical and Socioeconomic Driving Forces of Land‐Use and Land‐Cover Changes: A Case Study of Wuhan City, China (2016, neutral): “It originated in the social sciences and became popular in many fields including first chemometrics [31], sensory evaluation [32], and statistics [33] and ecology [34].”