Anzeige
Erschienen in:
2012 | OriginalPaper | Buchkapitel
Large Scale Multinomial Inferences and Its Applications in Genome Wide Association Studies
verfasst von : Chuanhai Liu, Jun Xie
Erschienen in: Belief Functions: Theory and Applications
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Statistical analysis of multinomial counts with a large number
K
of categories and a small number
n
of sample size is challenging to both frequentist and Bayesian methods and requires thinking about statistical inference at a very fundamental level. Following the framework of Dempster-Shafer theory of belief functions, a probabilistic inferential model is proposed for this “large
K
and small
n
” problem. Using a data-generating device, the inferential model produces probability triplet (
p
,
q
,
r
) for an assertion conditional on observed data. The probabilities
p
and
q
are
for
and
against
the truth of the assertion, whereas
r
= 1-
p
−
q
is the remaining probability called the probability of “don’t know”. The new inference method is applied in a genome-wide association study with very-high-dimensional count data, to identify association between genetic variants to a disease Rheumatoid Arthritis.