Generic framework for high-dimensional fixed-effects ANOVA
نویسندگان
چکیده
منابع مشابه
Generic framework for high-dimensional fixed-effects ANOVA
In functional genomics it is more rule than exception that experimental designs are used to generate the data. The samples of the resulting data sets are thus organized according to this design and for each sample many biochemical compounds are measured, e.g. typically thousands of gene-expressions or hundreds of metabolites. This results in high-dimensional data sets with an underlying experim...
متن کاملFixed effects testing in high-dimensional linear mixed models
Many scientific and engineering challenges – ranging from pharmacokinetic drug dosage allocation and personalized medicine to marketing mix (4Ps) recommendations – require an understanding of the unobserved heterogeneity in order to develop the best decision making-processes. In this paper, we develop a hypothesis test and the corresponding p-value for testing for the significance of the homoge...
متن کاملAn Efficient and Generic Hybrid Framework for High Dimensional Data Clustering
Clustering in high dimensional space is a difficult problem which is recurrent in many fields of science and engineering, e.g., bioinformatics, image processing, pattern reorganization and data mining. In high dimensional space some of the dimensions are likely to be irrelevant, thus hiding the possible clustering. In very high dimensions it is common for all the objects in a dataset to be near...
متن کاملA Generic High-Quality Meshing Framework
Josef Weinbub, Johann Cervenka,Karl Rupp, Siegfried Selberherr 1 Graduate Research Assistant and Presenting Author, Institute for Microelectronics, TU Wien, Vienna, Austria e-mail: [email protected] 2 Postdoctoral Research Associate, Institute for Microelectronics, TU Wien 3 Graduate Research Assistant, Institute for Microelectronics and Institute for Analysis and Scientific Computing, T...
متن کاملA Framework for Exploring High-Dimensional Geometry
To extract useful information from high-dimensional geometric or structural data, we must find low-dimensional projections that are informative and interesting to look at. The conventional, manual-interaction methods used for this purpose are ineffective when the dimensionality of the data is high, or when the geometric models are complex. Standard methods for determining useful low-dimensional...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Briefings in Bioinformatics
سال: 2011
ISSN: 1467-5463,1477-4054
DOI: 10.1093/bib/bbr071