Skip to main content

Semiparametric methods for response-selective and missing data problems in regression

Buy Article:

$43.00 plus tax (Refund Policy)

Suppose that data are generated according to the model f(y|x; θ) g(x), where y is a response and x are covariates. We derive and compare semiparametric likelihood and pseudolikelihood methods for estimating θ for situations in which units generated are not fully observed and in which it is impossible or undesirable to model the covariate distribution. The probability that a unit is fully observed may depend on y, and there may be a subset of covariates which is observed only for a subsample of individuals. Our key assumptions are that the probability that a unit has missing data depends only on which of a finite number of strata that (y, x) belongs to and that the stratum membership is observed for every unit. Applications include case–control studies in epidemiology, field reliability studies and broad classes of missing data and measurement error problems. Our results make fully efficient estimation of θ feasible, and they generalize and provide insight into a variety of methods that have been proposed for specific problems.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Biased sampling; Estimated likelihood; Estimation; Incomplete data; Pseudolikelihood

Document Type: Original Article

Affiliations: 1: University of Waterloo, Canada, 2: University of Auckland, New Zealand

Publication date: 1999-04-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more