Log-linear models for doubly sampled categorical data fitted by the EM algorithm
MA Espeland, CL Odoroff - Journal of the American Statistical …, 1985 - Taylor & Francis
MA Espeland, CL Odoroff
Journal of the American Statistical Association, 1985•Taylor & FrancisDouble-sampling experiments can be expressed as incomplete multiway contingency tables
and analyzed by using techniques appropriate for fitting log-linear models. The framework
described can be applied to experiments with any number of factors and measurement
devices. Maximum likelihood estimation via the EM algorithm leads to straightforward
expressions for covariances of estimates of the parameters and functions of the parameters.
and analyzed by using techniques appropriate for fitting log-linear models. The framework
described can be applied to experiments with any number of factors and measurement
devices. Maximum likelihood estimation via the EM algorithm leads to straightforward
expressions for covariances of estimates of the parameters and functions of the parameters.
Abstract
Double-sampling experiments can be expressed as incomplete multiway contingency tables and analyzed by using techniques appropriate for fitting log-linear models. The framework described can be applied to experiments with any number of factors and measurement devices. Maximum likelihood estimation via the EM algorithm leads to straightforward expressions for covariances of estimates of the parameters and functions of the parameters.
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