Maximum Likelihood Estimation of Simultaneous Pairwise Linear Structural Relationships
- 1 January 1995
- journal article
- research article
- Published by Wiley in Biometrical Journal
- Vol. 37 (6) , 673-689
- https://doi.org/10.1002/bimj.4710370604
Abstract
The problem of assessing the relative calibrations and relative accuracies of a set ofpinstruments, each designed to measure the same characteristic on a common group of individuals is considered by using the EM algorithm. As shown, the EM algorithm provides a general solution for this problem. Its implementation is simple and in its most general form requires no extra iterative procedures within the M step. One important feature of the algorithm in this set up is that the error variance estimates are always positive. Thus, it can be seen as a kind of restricted maximization procedure. The expected information matrix for the maximum likelihood estimators is derived, upon which the large sample estimated covariance matrix for the maximum likelihood estimators can be computed. The problem of testing hypothesis about the calibration lines can be approached by using the Wald statistics. The approach is illustrated by re‐analysing two data sets in the literature.Keywords
This publication has 7 references indexed in Scilit:
- Functional Comparative Calibration Using an EM AlgorithmPublished by JSTOR ,1992
- Measurement Error ModelsPublished by Wiley ,1987
- Restricted Maximum Likelihood Estimation of Bias and Reliability in the Comparison of Several Measuring MethodsPublished by JSTOR ,1981
- Comparative Calibration, Linear Structural Relationships and Congeneric MeasurementsBiometrics, 1978
- Maximum Likelihood from Incomplete Data Via the EM AlgorithmJournal of the Royal Statistical Society Series B: Statistical Methodology, 1977
- Simultaneous Pairwise Linear Structural RelationshipsBiometrics, 1969