.png){kind=icon}
Return
On function-on-function regression: partial least squares approach
.png){kind=icon}
OA
AI
DOI：10.1007/s10651-019-00436-1.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Functional data analysis tools, such as function-on-function regression models, have received considerable attention in various scientific fields because of their observed high-dimensional and complex data structures. Several statistical procedures, including least squares, maximum likelihood, and maximum penalized likelihood, have been proposed to estimate such function-on-function regression models. However, these estimation techniques produce unstable estimates in the case of degenerate functional data or are computationally intensive. To overcome these issues, we proposed a partial least squares approach to estimate the model parameters in the function-on-function regression model. In the proposed method, the B-spline basis functions are utilized to convert discretely observed data into their functional forms. Generalized cross-validation is used to control the degrees of roughness. The finite-sample performance of the proposed method was evaluated using several Monte-Carlo simulations and an empirical data analysis. The results reveal that the proposed method competes favorably with existing estimation techniques and some other available function-on-function regression models, with significantly shorter computational time.
Keywords:
Basis function
Functional data
NIPALS
Nonparametric smoothing
SIMPLS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
1.8
Papers:
1.0K
Citations:
1.1K
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveUsing basis expansions for estimating functional PLS regression Applications with chemometric dataOAAI
OAAIShareSave
ShareSaveShareSaveShareSave
View more