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Robust nonparametric estimation of monotone regression functions with interval-censored observations

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  • Ying Zhang
  • Gang Cheng
  • Wanzhu Tu

Abstract

type="main" xml:lang="en"> Nonparametric estimation of monotone regression functions is a classical problem of practical importance. Robust estimation of monotone regression functions in situations involving interval-censored data is a challenging yet unresolved problem. Herein, we propose a nonparametric estimation method based on the principle of isotonic regression. Using empirical process theory, we show that the proposed estimator is asymptotically consistent under a specific metric. We further conduct a simulation study to evaluate the performance of the estimator in finite sample situations. As an illustration, we use the proposed method to estimate the mean body weight functions in a group of adolescents after they reach pubertal growth spurt.

Suggested Citation

  • Ying Zhang & Gang Cheng & Wanzhu Tu, 2016. "Robust nonparametric estimation of monotone regression functions with interval-censored observations," Biometrics, The International Biometric Society, vol. 72(3), pages 720-730, September.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:3:p:720-730
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    Cited by:

    1. Chenghao Chu & Ying Zhang & Wanzhu Tu, 2020. "Stochastic functional estimates in longitudinal models with interval‐censored anchoring events," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 638-661, September.

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