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

Shanshan Lu, Jingjing Wu and Xuewen Lu ()
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Shanshan Lu: University of Calgary
Jingjing Wu: University of Calgary
Xuewen Lu: University of Calgary

Metrika: International Journal for Theoretical and Applied Statistics, 2019, vol. 82, issue 2, No 3, 173-194

Abstract: 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.

Keywords: B-splines; Counting process; Empirical process; Interval censored data; Monotonicity constraints; Semiparametric efficiency bound (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00184-018-0698-4

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