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A new generalized regression estimator and variance estimation for unequal probability sampling without replacement for missing data

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  • Nuanpan Lawson
  • Pachitjanut Siripanich

Abstract

The aim of this paper is to develop a new generalized regression estimator along with a method for estimating the variance of a population total using unequal probability sampling without replacement in the presence of nonresponse. We consider under less restricted situations where response probabilities are non-uniform and a sampling fraction can be both negligible and not negligible where both circumstances are more realistic in practice under the reverse framework. Theoretical proof shows that the new estimator is an almost unbiased estimator measured under the missing at random mechanism. Simulation studies and real data are used to exhibit some properties of the proposed estimators.

Suggested Citation

  • Nuanpan Lawson & Pachitjanut Siripanich, 2022. "A new generalized regression estimator and variance estimation for unequal probability sampling without replacement for missing data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(18), pages 6296-6318, September.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:18:p:6296-6318
    DOI: 10.1080/03610926.2020.1860224
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