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Small area estimates for cross‐classifications
Author(s) -
Zhang LiChun,
Chambers Raymond L.
Publication year - 2004
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/j.1369-7412.2004.05266.x
Subject(s) - small area estimation , estimator , estimation , best linear unbiased prediction , covariance , statistics , mathematics , computer science , unbiased estimation , econometrics , algorithm , artificial intelligence , engineering , selection (genetic algorithm) , systems engineering
Summary. We develop a class of log‐linear structural models that is suited to estimation of small area cross‐classified counts based on survey data. This allows us to account for various associ‐ ation structures within the data and includes as a special case the restricted log‐linear model underlying structure preserving estimation. The effect of survey design can be incorporated into estimation through the specification of an unbiased direct estimator and its associated covariance structure. We illustrate our approach by applying it to estimation of small area labour force characteristics in Norway.