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Robust models in probability sampling

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  • D. Firth
  • K. E. Bennett

Abstract

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.

Suggested Citation

  • D. Firth & K. E. Bennett, 1998. "Robust models in probability sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 3-21.
  • Handle: RePEc:bla:jorssb:v:60:y:1998:i:1:p:3-21
    DOI: 10.1111/1467-9868.00105
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    Cited by:

    1. Emmanuel Amissah & Katarzyna Świerczyńska, 2021. "Is Religion a Determinant of Financial Development?," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 27(3), pages 233-247, August.
    2. Donald P. Green & Winston Lin & Claudia Gerber, 2018. "Optimal Allocation of Interviews to Baseline and Endline Surveys in Place-Based Randomized Trials and Quasi-Experiments," Evaluation Review, , vol. 42(4), pages 391-422, August.
    3. Liu, Chun & Chen, Yang & Li, Shanmin & Sun, Liang & Yang, Mengjie, 2021. "Local political corruption and M&As," China Economic Review, Elsevier, vol. 69(C).
    4. Adhya Sumanta & Banerjee, Tathagata & Chattopadhyay, G., 2007. "Inference on Categorical Survey Response: A Predictive Approach," IIMA Working Papers WP2007-05-07, Indian Institute of Management Ahmedabad, Research and Publication Department.

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