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Two-level factorial designs for searching dispersion factors and estimating location main effects
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Optimal allocation of measurements in a linear calibration process
No full textA problem of allocation of measurements for a linear calibration process is considered in this article. It is assumed that a total of N measurements are made some of which may be measurements on two distinct standards, while the remaining measurements are on m different unknown specimens. We discuss allocation of the N measurements for the two standards and m unknown specimens based on A-optimality criterion, which is applied to asymptotic variances of maximum likelihood estimators for the true values of unknown specimens. It can be shown that the optimal allocation depends on the true values of unknown specimens. Hence, the user may resort to locally or Bayesian A-optimal measurement designs. Some practical solution is presented. Furthermore, the impact of prior on the allocation is also discussed. Copyright Springer-Verlag 2005Calibration model, A-optimality criterion, measurement design, Bayesian design,
Statistical designs for two-color microarray experiments involving technical replication
No full textA -expectation tolerance interval for general balanced mixed linear models
No full textA -expectation tolerance interval procedure is derived from the concept of generalized pivotal
quantity, which has been frequently used to obtain confidence intervals in situations where standard
procedures do not lead to useful solutions. The proposed procedure can be applied to general balanced
mixed linear models. Some practical examples are given to illustrate the proposed procedure. In
addition, detailed simulation studies are conducted to evaluate its performance, showing that it can
be recommended for use in practical applications.
© 2004 Elsevier B.V. All rights reserved
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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