Functional linear regression via canonical analysis
By G. He, H. Muller, Jane-ling Wang, Wenjing Yang. 2010
60 citations recorded by the source corpus.
We study regression models for the situation where both dependent and independent variables are square-integrable stochastic processes. Questions concerning the definition and existence of the corresponding functional linear regression models and some basic properties are explored for this situation. We derive a representation of the regression parameter function in terms of the canonical components of the processes involved. This representation establishes a connection between functional regression and functional canonical analysis and suggests alternative approaches for the implementation of functional linear regression analysis. A specific procedure for the estimation of the regression parameter function using canonical expansions is proposed and compared with an established functional principal component regression approach. As an example of an application, we present an analysis of mortality data for cohorts of medflies, obtained in experimental studies of aging and longevity.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/cf5783aee8cc4ef9b55a9b547c79c5b22dd4b8f3
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In Statistics and Probability
Citations in the corpus
- Functional Data Analysis (2016, methodology): “Despite the challenge with overfitting, FCCA can be employed to implement functional regression by using the canonical weight functions u k , and v k as bases to expand the regression coefficient function (He, M¨uller & Wang 2000; He et al. 2010).”