Inference based on regression estimator in double sampling

Ajit C. Tamhane*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The problem of hypothesis testing using the regression estimator in double sampling is considered. Test procedures are provided when the covariance matrix between the primary and the auxiliary variables is either partially known or completely unknown. For the latter case a new 'studentized' version of the regression estimator is proposed as a test statistic. The exact null distribution of this statistic is derived in a special case. An approximation to the null distribution is derived in the general case and studied by means of the Monte Carlo method. The problem of choosing between the double sample regression estimator and the single sample mean estimator is also discussed.

Original languageEnglish (US)
Pages (from-to)419-427
Number of pages9
JournalBiometrika
Volume65
Issue number2
DOIs
StatePublished - Aug 1978

Keywords

  • Bivariate normal distribution
  • Double sampling
  • Exact and approximate null distributions
  • Hypothesis test
  • Missing observations
  • Regression estimator

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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