61 research outputs found
Improving the INLA approach for approximate Bayesian inference for latent Gaussian models
We introduce a new copula-based correction for generalized linear mixed
models (GLMMs) within the integrated nested Laplace approximation (INLA)
approach for approximate Bayesian inference for latent Gaussian models. While
INLA is usually very accurate, some (rather extreme) cases of GLMMs with e.g.
binomial or Poisson data have been seen to be problematic. Inaccuracies can
occur when there is a very low degree of smoothing or "borrowing strength"
within the model, and we have therefore developed a correction aiming to push
the boundaries of the applicability of INLA. Our new correction has been
implemented as part of the R-INLA package, and adds only negligible
computational cost. Empirical evaluations on both real and simulated data
indicate that the method works well
Unsupervised empirical Bayesian multiple testing with external covariates
In an empirical Bayesian setting, we provide a new multiple testing method,
useful when an additional covariate is available, that influences the
probability of each null hypothesis being true. We measure the posterior
significance of each test conditionally on the covariate and the data, leading
to greater power. Using covariate-based prior information in an unsupervised
fashion, we produce a list of significant hypotheses which differs in length
and order from the list obtained by methods not taking covariate-information
into account. Covariate-modulated posterior probabilities of each null
hypothesis are estimated using a fast approximate algorithm. The new method is
applied to expression quantitative trait loci (eQTL) data.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS158 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Indirect genomic effects on survival from gene expression data
A novel methodology is presented for detecting and quantifying indirect effects on cancer survival mediated through several target genes of transcription factors in cancer microarray data
The Genomic HyperBrowser: inferential genomics at the sequence level
The immense increase in the generation of genomic scale data poses an unmet
analytical challenge, due to a lack of established methodology with the
required flexibility and power. We propose a first principled approach to
statistical analysis of sequence-level genomic information. We provide a
growing collection of generic biological investigations that query pairwise
relations between tracks, represented as mathematical objects, along the
genome. The Genomic HyperBrowser implements the approach and is available at
http://hyperbrowser.uio.no
Causal modeling and inference for electricity markets
How does dynamic price information flow among Northern European electricity
spot prices and prices of major electricity generation fuel sources? We use
time series models combined with new advances in causal inference to answer
these questions. Applying our methods to weekly Nordic and German electricity
prices, and oil, gas and coal prices, with German wind power and Nordic water
reservoir levels as exogenous variables, we estimate a causal model for the
price dynamics, both for contemporaneous and lagged relationships. In
contemporaneous time, Nordic and German electricity prices are interlinked
through gas prices. In the long run, electricity prices and British gas prices
adjust themselves to establish the equlibrium price level, since oil, coal,
continental gas and EUR/USD are found to be weakly exogenous
The Genomic HyperBrowser: an analysis web server for genome-scale data
The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome
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