Abstract:
:We propose a flexible and computationally efficient penalized estimation method for a semi-parametric linear transformation model with current status data. To facilitate model fitting, the unknown monotone function is approximated by monotone B-splines, and a computationally efficient hybrid algorithm involving the Fisher scoring algorithm and the isotonic regression is developed. A goodness-of-fit test and model diagnostics are also considered. The asymptotic properties of the penalized estimators are established, including the optimal rate of convergence for the function estimator and the semi-parametric efficiency for the regression parameter estimators. An extensive numerical experiment is conducted to evaluate the finite-sample properties of the penalized estimators, and the methodology is further illustrated with two real studies.
journal_name
Stat Methods Med Resjournal_title
Statistical methods in medical researchauthors
Lu M,Liu Y,Li CSdoi
10.1177/0962280218820406subject
Has Abstractpub_date
2020-01-01 00:00:00pages
3-14issue
1eissn
0962-2802issn
1477-0334journal_volume
29pub_type
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