Partial least squares estimator for single‐index models

103 citations recorded by the source corpus.

How researchers see this paper

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

Understanding the Impact of Synergy in Multimedia Communications (2003, methodology): “Naik and Tsai (2000b) demonstrate that PLS yields consistent estimates even if model specification is unknown or incorrect.”
Extending the Akaike Information Criterion to Mixture Regression Models (2007, methodology): “…sample size is smaller than the number of variables (e.g., in chemometrics or bioinformatics), users can apply the partial least squares (PLS) estimator (e.g., Helland and Almøy 1994; Naik and Tsai 2000; Hastie, Tibshirani, and Friedman 2001) within each component of the mixture regression model.”
Sufficient Dimension Reduction via Inverse Regression (2005, background): “Naik and Tsai (2000) compared the performance of SIR to partial least squares in the context of a single-index model.”
Partial least squares prediction in high-dimensional regression (2019, methodology): “The same normality was used also by Naik and Tsai [30] in their asymptotic study of the fixed p case and by Chun and Keleş [6] for the case in which p and n both diverge.”