
doi: 10.2307/2556113
Precise criteria have been published recently for obtaining unbiased ratio estimators of structural parameters, defined in an n-dimensional opaque specimen, from observations in lower-dimensional sections. In this paper, the possibility is shown of obtaining linear unbiased estimators of minimum variance whenever the data can be described by a linear regression model through the origin. Estimation from singleand two-stage sampling is discussed; the former is illustrated by an example.
Linear regression; mixed models, area-weighted sampling, systematic sampling, Applications of statistics to biology and medical sciences; meta analysis, isotropic uniform random sampling, Sampling theory, sample surveys, stereology, minimum variance, weighted linear regression, AWR, IUR, Geometric probability and stochastic geometry, best linear unbiased estimators, integral geometry
Linear regression; mixed models, area-weighted sampling, systematic sampling, Applications of statistics to biology and medical sciences; meta analysis, isotropic uniform random sampling, Sampling theory, sample surveys, stereology, minimum variance, weighted linear regression, AWR, IUR, Geometric probability and stochastic geometry, best linear unbiased estimators, integral geometry
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