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.

A Marginal Likelihood Approach to Estimation in Frailty Models

K. F. Lam and Anthony Y. C. Kuk
Journal of the American Statistical Association
Vol. 92, No. 439 (Sep., 1997), pp. 985-990
DOI: 10.2307/2965562
Stable URL: http://www.jstor.org/stable/2965562
Page Count: 6
  • Download ($14.00)
  • Cite this Item
A Marginal Likelihood Approach to Estimation in Frailty Models
Preview not available

Abstract

A marginal likelihood approach is proposed for estimating the parameters in a frailty model using clustered survival data. To overcome the analytic intractability of the marginal likelihood function, we propose a Monte Carlo approximation using the technique of importance sampling. Implementation is by means of simulations from the uniform distribution. The suggested method can cope with censoring and unequal cluster sizes and can be applied to any frailty distribution with explicit Laplace transform. We concentrate on a two-parameter family that includes the gamma, inverse Gaussian, and positive stable distributions as special cases. The method is illustrated using data from an animal carcinogenesis experiment and validated in a simulation study.

Page Thumbnails

  • Thumbnail: Page 
985
    985
  • Thumbnail: Page 
986
    986
  • Thumbnail: Page 
987
    987
  • Thumbnail: Page 
988
    988
  • Thumbnail: Page 
989
    989
  • Thumbnail: Page 
990
    990