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It is not our intention to comprehensively review analytic methodologies for zero-inflated data.
Development of efficient analytic methodologies for combining microarray results is a major challenge in gene expression analysis.
In this study we utilized two related analytic methodologies for identifying the underlying patterns within our dataset: principal components analysis (PCA) and factor analysis (FA).
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A wide range of statistical and computational strategies have been developed for modeling, mining, predicting and interpreting binary synthetic lethal/sick genetic interactions with other interaction information [17] [29], yet only a limited effort has been devoted to developing generic analytic methodology for the needs of the large-scale quantitative genetic interaction experiments.
For example, competitive-binding immunoassay may be a satisfactory analytic methodology for many clinical applications, but it is based on a single measurement that has known susceptibility to cross-reactions, and it requires external calibration.
The main analytic methodology for the primary outcomes, GLMMs via maximum likelihood estimation, implicitly accommodates missingness when it is due either to treatment, to prior outcome, or to other baseline covariates included in the model, defined as 'missing at random' [ 53].
Meta-analytic methodologies for such kind of data have been developed to summarize sensitivity and specificity separately or jointly in a fixed or random effects context, for example [ 1- 6].
The water sampling and analytic methodologies from RFTS have been described previously (Savitz et al. 2005, 2006).
The turn of the century brought new assay platforms and analytic methodologies that would allow for whole genome searches to be performed efficiently in unrelated individuals.
Phenomenology is not the only source of analytic methodologies outside those of the analytic tradition.
As far as understanding analytic methodologies was concerned, the humanist repudiation of scholastic logic muddied the waters.
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