Name
Abstract | ||
|---|---|---|
Current status data often arise in survival analysis and reliability studies, when a continuous response is reduced to an
indicator of whether the response is greater or less than an observed random threshold value. This article considers a partial
linear model with current status data. A sieve least squares estimator is proposed to estimate both the regression parameters
and the nonparametric function. This paper shows, under some mild condition, that the estimators are strong consistent. Moreover,
the parameter estimators are normally distributed, while the nonparametric component achieves the optimal convergence rate.
Simulation studies are carried out to investigate the performance of the proposed estimates. For illustration purposes, the
method is applied to a real dataset from a study of the calcification of the hydrogel intraocular lenses, a complication of
cataract treatment. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s11424-011-8050-3 | J. Systems Science & Complexity |
Keywords | Field | DocType |
current status data,sieve least squares estimator,strong consistent.,convergence rate,partial linear model,strong consistency,parameter estimation,normal distribution,survival analysis | Least squares,Mathematical optimization,Ordinary least squares,Generalized least squares,Simple linear regression,Non-linear least squares,Total least squares,Statistics,Linear least squares,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
24 | 2 | 15597067 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||