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Efficient estimation of the varying-coefficient partially linear proportional odds model with current status data

Author

Listed:
  • Shanshan Lu

    (University of Calgary)

  • Jingjing Wu

    (University of Calgary)

  • Xuewen Lu

    (University of Calgary)

Abstract

We consider a varying-coefficient partially linear proportional odds model with current status data. This model enables one to examine the extent to which some covariates interact nonlinearly with an exposure variable, while other covariates present linear effects. B-spline approach and sieve maximum likelihood estimation method are used to get an integrated estimate for the linear coefficients, the varying-coefficient functions and the baseline function. The proposed parameter estimators are proved to be semiparametrically efficient and asymptotically normal, and the estimators for the nonparametric functions achieve the optimal rate of convergence. Simulation studies and a real data analysis are used for assessment and illustration.

Suggested Citation

  • Shanshan Lu & Jingjing Wu & Xuewen Lu, 2019. "Efficient estimation of the varying-coefficient partially linear proportional odds model with current status data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(2), pages 173-194, March.
    • Handle: RePEc:spr:metrik:v:82:y:2019:i:2:d:10.1007_s00184-018-0698-4
    DOI: 10.1007/s00184-018-0698-4
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    References listed on IDEAS

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