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    Latent group detection in functional partially linear regression models

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    Thumbnail
    Name:
    biom.13557.pdf
    Size:
    475.1Kb
    Format:
    PDF
    Description:
    Accepted manuscript
    Embargo End Date:
    2022-09-05
    Download
    Type
    Article
    Authors
    Wang, Huixia Judy cc
    Sun, Ying cc
    Wang, Huixia Judy
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Environmental Statistics Group
    Statistics Program
    Date
    2021-09-14
    Embargo End Date
    2022-09-05
    Submitted Date
    2020-06-27
    Permanent link to this record
    http://hdl.handle.net/10754/670949
    
    Metadata
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    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
    Publisher
    Wiley
    Journal
    Biometrics
    DOI
    10.1111/biom.13557
    Additional Links
    https://onlinelibrary.wiley.com/doi/10.1111/biom.13557
    ae974a485f413a2113503eed53cd6c53
    10.1111/biom.13557
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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