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.
| Original language | English (US) |
|---|---|
| Journal | Statistical Methods in Medical Research |
| DOIs | |
| State | Accepted/In press - Jan 1 2018 |
| Externally published | Yes |
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Keywords
- Current status data
- efficient estimation
- goodness-of-fit
- penalized spline
- transformation model
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
- Health Information Management
Cite this
Efficient estimation of a linear transformation model for current status data via penalized splines. / Lu, Minggen; Liu, Yan; Li, Chin-Shang.
In: Statistical Methods in Medical Research, 01.01.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Efficient estimation of a linear transformation model for current status data via penalized splines
AU - Lu, Minggen
AU - Liu, Yan
AU - Li, Chin-Shang
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Current status data
KW - efficient estimation
KW - goodness-of-fit
KW - penalized spline
KW - transformation model
UR - http://www.scopus.com/inward/record.url?scp=85060441411&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060441411&partnerID=8YFLogxK
U2 - 10.1177/0962280218820406
DO - 10.1177/0962280218820406
M3 - Article
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
SN - 0962-2802
ER -