We propose a new procedure to estimate the index parameter and link function of single-index models, where the response variable is subject to fixed censoring. Under some regularity conditions, we show that the estimated index parameter is root-n consistent and asymptotically normal, and the estimated nonparametric link function achieves the optimal convergence rate and is asymptotically normal. In addition, we propose a linearity testing method for the nonparametric link function. A simulation study shows that the proposed procedures perform well in finite-sample experiments. An application to an HIV data set is presented for illustrative purposes.
|Original language||English (US)|
|Number of pages||15|
|State||Published - Apr 2020|
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty