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作者 KEEN, KEVIN JOHN
書名 ESTIMATION OF INTRACLASS AND INTERCLASS CORRELATIONS
國際標準書號 9780315396791
book jacket
附註 Source: Dissertation Abstracts International, Volume: 48-12, Section: B, page: 3612
Thesis (Ph.D.)--University of Toronto (Canada), 1987
Estimation of the correlation of measurements among family members began with the work of Sir Francis Galton. He proposed the interclass correlation to assess the degree to which sons' heights are passed down genetically from their fathers. Later, Karl Pearson formulated the intraclass correlation to measure the degree to which sons of the same father are alike in height. Sir Ronald Fisher improved upon Pearson's estimator of the intraclass correlation through the analysis of variance (ANOVA) procedure
A clarification is given of the relationships among the different methods of point and interval estimation of the intraclass correlation coefficient in the unbalanced one-way random-effects model. On the basis of an extensive Monte Carlo simulation done to compare the various methods, a version of the point estimator originally developed by Fisher for the balanced case and the interval estimator based on this point estimator and its asymptotic variance are both recommended. The maximum likelihood method and Wald's method are both shown by simulation to provide unacceptable interval estimators for small values of the intraclass correlation parameter
An estimator based on Fisher's approach is developed for the interclass correlation coefficient using arbitrary weights. The asymptotic variance of this estimator is derived. A Monte Carlo simulation is done to evaluate different weighting schemes. By a separate Monte Carlo simulation, these estimators are found not to differ greatly from the maximum likelihood estimator in terms of asymptotic efficiency
Using the method of weighted sums-of-squares, estimators are developed for the generalization of intraclass and interclass correlations for a random vector measured on the members of an arbitrary number of classes within independent groups. With the derivation of the asymptotic variances of these estimators of the intraclass and interclass correlation matrices, a large-sample alternative to interval estimation by the method of maximum likelihood is proposed and illustrated with a well-known example
School code: 0779
Host Item Dissertation Abstracts International 48-12B
主題 Statistics
0463
Alt Author University of Toronto (Canada)
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