Pages that link to "Item:Q3526159"

The following pages link to Small-sample estimation of negative binomial dispersion, with applications to SAGE data (Q3526159):

Displaying 28 items.

• A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data (Q306683) (← links)
• Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping (Q312910) (← links)
• Growth estimators and confidence intervals for the mean of negative binomial random variables with unknown dispersion (Q361598) (← links)
• On the existence of maximum likelihood estimators in Poisson-gamma HGLM and negative binomial regression model (Q375217) (← links)
• Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data (Q523926) (← links)
• Simultaneous estimation of negative binomial dispersion parameters (Q645412) (← links)
• HmmSeq: a hidden Markov model for detecting differentially expressed genes from RNA-seq data (Q746680) (← links)
• Detecting differentially expressed genes with RNA-seq data using backward selection to account for the effects of relevant covariates (Q906078) (← links)
• Estimating the negative binomial dispersion parameter with highly stratified surveys (Q963908) (← links)
• Single-gene negative binomial regression models for RNA-seq data with higher-order asymptotic inference (Q1747465) (← links)
• Detecting rare and faint signals via thresholding maximum likelihood estimators (Q1750291) (← links)
• Uncertainty and error in SARS-CoV-2 epidemiological parameters inferred from population-level epidemic models (Q2109314) (← links)
• Uniformly consistently estimating the proportion of false null hypotheses via Lebesgue-Stieltjes integral equations (Q2274975) (← links)
• Sample size calculations for the differential expression analysis of RNA-seq data using a negative binomial regression model (Q2324965) (← links)
• A new approach to generating virtual samples to enhance classification accuracy with small data -- a case of bladder cancer (Q2686756) (← links)
• Modeling overdispersion heterogeneity in differential expression analysis using mixtures (Q2827191) (← links)
• An optimal test with maximum average power while controlling FDR with application to RNA-seq data (Q2861943) (← links)
• Removing technical variability in RNA-seq data using conditional quantile normalization (Q3303808) (← links)
• A novel Bayesian regression model for counts with an application to health data (Q5035755) (← links)
• A finite mixture approach to joint clustering of individuals and multivariate discrete outcomes (Q5106920) (← links)
• Profile likelihood-based confidence interval for the dispersion parameter in count data (Q5126986) (← links)
• Inference concerning a common dispersion of several treatment groups in the analysis of over/underdispersed count data (Q5420231) (← links)
• A model for analyzing clustered occurrence data (Q6079476) (← links)
• Classification of RNA-Seq data via Gaussian copulas (Q6540511) (← links)
• Marginal likelihood estimation for the negative binomial INGARCH model (Q6562733) (← links)
• A flexible model for correlated count data, with application to multicondition differential expression analyses of single-cell RNA sequencing data (Q6616406) (← links)
• Two-sample \(t_\alpha\)-test for testing hypotheses in small-sample experiments (Q6636203) (← links)
• Deconvolution analysis of spatial transcriptomics by multiplicative-additive Poisson-gamma models (Q6665544) (← links)