General power and sample size calculations for high-dimensional genomic data

DOI10.1515/sagmb-2012-0046zbMath1311.92015OpenAlexW2067542637WikidataQ30660865 ScholiaQ30660865MaRDI QIDQ2344242

Yanyan Li

Publication date: 13 May 2015

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-2012-0046

zbMATH Keywords

discrete inverse problemhigh-dimensional generalized linear modelsdensity of effect-sizesnon-negative conjugate gradients algorithm

Mathematics Subject Classification ID

General biostatistics (92B15) Sampling theory, sample surveys (62D05) Genetics and epigenetics (92D10)

Related Items (1)

Sample size reassessment for a two-stage design controlling the false discovery rate

Uses Software

• R
• Bioconductor
• edgeR
• SemiPar
• CGHpower

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On the Benjamini-Hochberg method
• A statistical perspective on ill-posed inverse problems (with discussion)
• The positive false discovery rate: A Bayesian interpretation and the \(q\)-value
• Quasi-Newton approach to nonnegative image restorations
• Tikhonov regularization with nonnegativity constraint
• Estimating Effect Sizes of Differentially Expressed Genes for Power and Sample-Size Assessments in Microarray Experiments
• Pitfalls in the Numerical Solution of Linear Ill-Posed Problems
• Introduction to Nonparametric Regression
• Power/Sample Size Calculations for Generalized Linear Models
• The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems
• Semiparametric Regression
• On Power and Sample Size Calculations for Likelihood Ratio Tests in Generalized Linear Models
• A Statistical View of Some Chemometrics Regression Tools
• Improved Estimation of the Noncentrality Parameter Distribution from a Large Number of t‐Statistics, with Applications to False Discovery Rate Estimation in Microarray Data Analysis
• Empirical Bayes Estimates for Large-Scale Prediction Problems
• Discrete Inverse Problems
• Estimating the False Discovery Rate Using Nonparametric Deconvolution
• Approximate Sample Size Calculations with Microarray Data: An Illustration
• Exploring the Information in p‐Values for the Analysis and Planning of Multiple‐Test Experiments
• Estimating the Proportion of True Null Hypotheses, with application to DNA Microarray Data
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Methods of conjugate gradients for solving linear systems
• The elements of statistical learning. Data mining, inference, and prediction

This page was built for publication: General power and sample size calculations for high-dimensional genomic data