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Multistage Sampling Designs and Estimating Equations
Author(s) -
Whittemore Alice S.
Publication year - 1997
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00084
Subject(s) - statistics , stratified sampling , sampling (signal processing) , mathematics , simple random sample , sampling design , parametric statistics , multistage sampling , sample (material) , poisson sampling , maximum likelihood , computer science , importance sampling , slice sampling , monte carlo method , population , chemistry , demography , filter (signal processing) , chromatography , sociology , computer vision
In some applications it is cost efficient to sample data in two or more stages. In the first stage a simple random sample is drawn and then stratified according to some easily measured attribute. In each subsequent stage a random subset of previously selected units is sampled for more detailed and costly observation, with a unit’s sampling probability determined by its attributes as observed in the previous stages. This paper describes multistage sampling designs and estimating equations based on the resulting data. Maximum likelihood estimates (MLEs) and their asymptotic variances are given for designs using parametric models. Horvitz–Thompson estimates are introduced as alternatives to MLEs, their asymptotic distributions are derived and their strengths and weaknesses are evaluated. The designs and the estimates are illustrated with data on corn production.