Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

Sieve Maximum Likelihood Estimator for Semiparametric Regression Models with Current Status Data

Hongqi Xue, K. F. Lam and Guoying Li
Journal of the American Statistical Association
Vol. 99, No. 466 (Apr., 2004), pp. 346-356
Stable URL: http://www.jstor.org/stable/27590391
Page Count: 11
  • Download ($14.00)
  • Cite this Item
Sieve Maximum Likelihood Estimator for Semiparametric Regression Models with Current Status Data
Preview not available

Abstract

In a randomized controlled clinical trial study where the response variable of interest is the time to occurrence of a certain event, it is often too expensive or even impossible to observe the exact time. However, the current status of the subject at a random time of inspection is much more natural, feasible, and practical in terms of cost-effectiveness. This article considers a semiparametric regression model that consists of parametric and nonparametric regression components. A sieve maximum likelihood estimator (MLE) is proposed to estimate the regression parameter, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically efficient and normally distributed, and the estimator of the nonparametric function has an optimal convergence rate. Simulation studies were carried out to investigate the performance of the proposed method. For illustration purposes, the method is applied to a dataset from a study of the calcification of the hydrogel intraocular lenses, a complication of cataract treatment.

Page Thumbnails

  • Thumbnail: Page 
346
    346
  • Thumbnail: Page 
347
    347
  • Thumbnail: Page 
348
    348
  • Thumbnail: Page 
349
    349
  • Thumbnail: Page 
350
    350
  • Thumbnail: Page 
351
    351
  • Thumbnail: Page 
352
    352
  • Thumbnail: Page 
353
    353
  • Thumbnail: Page 
354
    354
  • Thumbnail: Page 
355
    355
  • Thumbnail: Page 
356
    356