Latent group detection in functional partially linear regression models

Wang, Huixia Judy; Sun, Ying

Latent group detection in functional partially linear regression models

Files

biom.13557.pdf (475.14 KB)

Embargo End Date

2022-09-05

Type

Article

Authors

Wang, Huixia Judy

Sun, Ying

Wang, Huixia Judy

KAUST Department

Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Environmental Statistics Group
Statistics Program

Date

2021-09-14

Submitted Date

2020-06-27

Abstract

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.13557