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(20100302) AUEB SEMINARS : The Effective Sample Size to Measure the Amount of Information in a Correlated Data Setting, Athens - Greece

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(20100302) AUEB SEMINARS : The Effective Sample Size to Measure the Amount of Information in a Correlated Data Setting, Athens - Greece

Δημοσίευση από grstats Την / Το Δευ 22 Φεβ 2010 - 11:13

Την Τρίτη 02 Μαρτίου 2010, και ωρα 15.00-16.00 θα πραγματοποιηθεί σεμινάριο από την
Καθ Christel Faes, από το Univeristy of Hasselt, Belgium με θέμα

The Effective Sample Size to Measure the Amount of Information in a Correlated Data Setting

Το σεμινάριο θα γινει στην αιθουσα 607, στο κτιρίο του μεταπτυχιακού (Ευελπίδων και Λευκάδος)

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The Effective Sample Size to Measure the Amount of Information in a Correlated Data Setting.

Christel Faes,
Center for Statistics, Hasselt University, Belgium

Correlated data frequently arise in contexts such as, for example, repeated
measures and meta-analysis. The amount of information in such data de-
pends not only on the sample size, but also on the structure and strength
of the correlations among observations from the same independent block. A
general concept is discussed, the effective sample size, as a way of quantifying
the amount of information in such data. It is defined as the sample size one
would need in an independent sample to equal the amount of information in
the actual correlated sample. This concept is widely applicable, for Gaus-
sian data and beyond, and provides important insight. For example, it helps
explaining why fixed-effects and random-effects inferences of meta-analytic
data can be so radically divergent. Further, we show that in some cases
the amount of information is bounded, even when the number of measures
per independent block approaches infinity. We use the method to devise
a new denominator degrees-of-freedom method for fixed-effects testing. It
is compared to the classical Satterthwaite and Kenward-Roger methods for
performance and, more importantly, to enhance insight. A key feature of the
proposed degrees-of-freedom method is that it, unlike the others, can be used
for non-Gaussian data too; it is exemplified for binary data. The effective
sample size also yields insights when interested in the effect of e.g. patient
allocation schemes in clinical trials on the power of a test.

References
Faes, C., Molenberghs, G., Aerts, M., Verbeke, G., Kenward, M.G. (2009) The Effective Sample Size and
a Novel Small Sample Degrees of Freedom Method, The American Statistician, 63, 389-399.
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