The Annals of Statistics

Asymptotic Theory of Systematic Sampling

Ronaldo Iachan

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Abstract

The main purpose of this paper is the development of an asymptotic theory of systematic sampling from a stochastic population. The superpopulation model assumed is that the population arises from a second-order stationary process. A comparison among the multiple random start systematic sampling schemes is made in terms of the limiting expected variance of the sample mean. Asymptotic normality of the systematic sampling mean is obtained, both unconditionally and conditionally on the given population. Finally, the asymptotic behavior of confidence intervals based on two distinct variance estimators is studied.

Article information

Source
Ann. Statist., Volume 11, Number 3 (1983), 959-969.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176346261

Digital Object Identifier
doi:10.1214/aos/1176346261

Mathematical Reviews number (MathSciNet)
MR707945

Zentralblatt MATH identifier
0519.62005

JSTOR
links.jstor.org

Subjects
Primary: 62D05: Sampling theory, sample surveys
Secondary: 62F25: Tolerance and confidence regions 62E20: Asymptotic distribution theory

Keywords
Multiple random start systematic sampling limiting expected variance asymptotic normality variance estimator

Citation

Iachan, Ronaldo. Asymptotic Theory of Systematic Sampling. Ann. Statist. 11 (1983), no. 3, 959--969. doi:10.1214/aos/1176346261. https://projecteuclid.org/euclid.aos/1176346261


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