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

Authors
  • Lu, Shanshan1
  • Wu, Jingjing1
  • Lu, Xuewen1
  • 1 University of Calgary, Department of Mathematics and Statistics, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada , Calgary (Canada)
Type
Published Article
Journal
Metrika
Publisher
Springer Berlin Heidelberg
Publication Date
Dec 01, 2018
Volume
82
Issue
2
Pages
173–194
Identifiers
DOI: 10.1007/s00184-018-0698-4
Source
Springer Nature
Keywords
License
Yellow

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.

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