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A Semiparametric Change-Point Regression Model for Longitudinal Observations

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  • Haipeng Xing
  • Zhiliang Ying

Abstract

Many longitudinal studies involve relating an outcome process to a set of possibly time-varying covariates, giving rise to the usual regression models for longitudinal data. When the purpose of the study is to investigate the covariate effects when experimental environment undergoes abrupt changes or to locate the periods with different levels of covariate effects, a simple and easy-to-interpret approach is to introduce change-points in regression coefficients. In this connection, we propose a semiparametric change-point regression model, in which the error process (stochastic component) is nonparametric and the baseline mean function (functional part) is completely unspecified, the observation times are allowed to be subject specific, and the number, locations, and magnitudes of change-points are unknown and need to be estimated. We further develop an estimation procedure that combines the recent advance in semiparametric analysis based on counting process argument and multiple change-points inference and discuss its large sample properties, including consistency and asymptotic normality, under suitable regularity conditions. Simulation results show that the proposed methods work well under a variety of scenarios. An application to a real dataset is also given.

Suggested Citation

  • Haipeng Xing & Zhiliang Ying, 2012. "A Semiparametric Change-Point Regression Model for Longitudinal Observations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1625-1637, December.
  • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:500:p:1625-1637
    DOI: 10.1080/01621459.2012.712425
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    Cited by:

    1. Bouzebda, Salim & Ferfache, Anouar Abdeldjaoued, 2023. "Asymptotic properties of semiparametric M-estimators with multiple change points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    2. Minya Xu & Ping-Shou Zhong & Wei Wang, 2016. "Detecting Variance Change-Points for Blocked Time Series and Dependent Panel Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 213-226, April.
    3. Haipeng Xing & Yang Yu, 2018. "Firm’s Credit Risk in the Presence of Market Structural Breaks," Risks, MDPI, vol. 6(4), pages 1-16, December.

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