A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments
From MaRDI portal
Publication:521444
DOI10.1515/sagmb-2015-0082zbMath1359.92034OpenAlexW2523382358WikidataQ36140204 ScholiaQ36140204MaRDI QIDQ521444
Klaus Jung, Tim Beißbarth, Frank Kramer, Jochen Kruppa
Publication date: 11 April 2017
Published in: Statistical Applications in Genetics and Molecular Biology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1515/sagmb-2015-0082
Related Items (1)
Uses Software
Cites Work
- Computing the nearest correlation matrix--a problem from finance
- Multivariate negative binomial models for insurance claim counts
- Optimal estimation of a large-dimensional covariance matrix under Stein's loss
- Finite mixtures of multivariate Poisson distributions with application
- Nonparametric Bayes modelling of count processes
- An Algorithm for Fast Generation of Bivariate Poisson Random Vectors
- Testing Against a High Dimensional Alternative
- Interval censored data: A note on the nonparametric maximum likelihood estimator of the distribution function
- A Potential for Bias When Rounding in Multiple Imputation
- Rounding non-binary categorical variables following multivariate normal imputation: evaluation of simple methods and implications for practice
- The huge Package for High-dimensional Undirected Graph Estimation in R
- On generating multivariate Poisson data in management science applications
- Accurate Ranking of Differentially Expressed Genes by a Distribution-Free Shrinkage Approach
This page was built for publication: A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments