Latent group detection in functional partially linear regression models
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Science and Engineering (CEMSE) DivisionEnvironmental Statistics Group
Statistics Program
Date
2021-09-14Embargo End Date
2022-09-05Submitted Date
2020-06-27Permanent link to this record
http://hdl.handle.net/10754/670949
Metadata
Show full item recordAbstract
In this paper, we propose a functional partially linear regression model with latent group structures to accommodate the heterogeneous relationship between a scalar response and functional covariates. The proposed model is motivated by a salinity tolerance study of barley families, whose main objective is to detect salinity tolerant barley plants. Our model is flexible, allowing for heterogeneous functional coefficients while being efficient by pooling information within a group for estimation. We develop an algorithm in the spirit of the K-means clustering to identify latent groups of the subjects under study. We establish the consistency of the proposed estimator, derive the convergence rate and the asymptotic distribution, and develop inference procedures. We show by simulation studies that the proposed method has higher accuracy for recovering latent groups and for estimating the functional coefficients than existing methods. The analysis of the barley data shows that the proposed method can help identify groups of barley families with different salinity tolerant abilities.Citation
Wang, W., Sun, Y., & Wang, H. J. (2021). Latent group detection in functional partially linear regression models. Biometrics. doi:10.1111/biom.13557Publisher
WileyJournal
BiometricsAdditional Links
https://onlinelibrary.wiley.com/doi/10.1111/biom.13557ae974a485f413a2113503eed53cd6c53
10.1111/biom.13557