Skip to main content

Robust models in probability sampling

Buy Article:

$51.00 plus tax (Refund Policy)


In the estimation of a population mean or total from a random sample, certain methods based on linear models are known to be automatically design consistent, regardless of how well the underlying model describes the population. A sufficient condition is identified for this type of robustness to model failure; the condition, which we call ‘internal bias calibration’, relates to the combination of a model and the method used to fit it. Included among the internally bias-calibrated models, in addition to the aforementioned linear models, are certain canonical link generalized linear models and nonparametric regressions constructed from them by a particular style of local likelihood fitting. Other models can often be made robust by using a suboptimal fitting method. Thus the class of model-based, but design consistent, analyses is enlarged to include more realistic models for certain types of survey variable such as binary indicators and counts. Particular applications discussed are the estimation of the size of a population subdomain, as arises in tax auditing for example, and the estimation of a bootstrap tail probability.

Keywords: Auditing; Bias calibration; Bootstrap acceleration; Control variate; Finite population; Generalized linear model; Importance sampling; Instrumental variable; Local likelihood; Logistic regression; Smoothing; Spline; Stratification; Survey sampling

Document Type: Original Article


Affiliations: 1: University of Oxford, UK, 2: Medical Research Council Institute of Hearing Research, Nottingham, UK

Publication date: January 1, 1998


Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
Cookie Policy
ingentaconnect 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
Real Time Web Analytics