An adaptive significance threshold criterion for massive multiple hypotheses testing

  title={An adaptive significance threshold criterion for massive multiple hypotheses testing},
  author={Cheng Cheng},
  journal={arXiv: Statistics Theory},
  • Cheng Cheng
  • Published 27 October 2006
  • Mathematics
  • arXiv: Statistics Theory
This research deals with massive multiple hypothesis testing. First regarding multiple tests as an estimation problem under a proper population model, an error measurement called Erroneous Rejection Ratio (ERR) is introduced and related to the False Discovery Rate (FDR). ERR is an error measurement similar in spirit to FDR, and it greatly simplifies the analytical study of error properties of multiple test procedures. Next an improved estimator of the proportion of true null hypotheses and a… 

Figures and Tables from this paper

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

Simulations show that, under a variety of situations, the density estimates are closer to the underlying truth and the FDR estimates are improved compared with alternative methods.

A parametric model to estimate the proportion from true null using a distribution for p-values

A Framework for Monte Carlo based Multiple Testing

A framework for multiple testing with tests whose p‐values cannot be computed explicitly but can be approximated using Monte Carlo simulation is introduced by providing a generic algorithm for a general multiple testing procedure and is applicable to a general class of step‐up and step‐down procedures.

Testing Differential Expression in Nonoverlapping gene Pairs: a New Perspective for the Empirical Bayes Method

The proposed modification of the empirical Bayes method leads to significant improvements in its performance and the new paradigm arising from the existence of the delta-sequence in biological data offers considerable scope for future developments.

Improving statistical inference for gene expression profiling data by borrowing information

This thesis discusses statistical issues associated with gene expression profiling experiments and develops new statistical methods to tackle some of these problems and develops a novel combination of two statistical techniques to by-pass the curse of dimensionality problem.

Literature aided determination of data quality and statistical significance threshold for gene expression studies

Novel literature based approaches to integrate functional information in analysis of gene expression data are developed and robust and objective literature-based methods to evaluate the biological support for gene expression experiments and to determine the appropriate statistical significance threshold are developed.

Pharmacogenomics of intracellular methotrexate polyglutamates in patients' leukemia cells in vivo.

These findings provide insights into mechanisms underlying interpatient differences in intracellular accumulation of MTXPG in leukemia cells and its antileukemic effects.

Genetic Polymorphisms Associated with Vincristine Pharmacokinetics and Vincristine-Induced Peripheral Neuropathy in Pediatric Oncology Patients

Simple Summary Vincristine is a type of chemotherapy that is often used in the treatment of children with cancer. The main side effect of vincristine is nerve damage. Patients experience symptoms

Integrative genomic analyses reveal mechanisms of glucocorticoid resistance in acute lymphoblastic leukemia.

Manipulation of CELSR2 recapitulated glucocorticoid resistance in human leukemia cell lines and revealed a synergistic drug combination (prednisolone and venetoclax) that mitigated resistance in mouse xenograft models.



The positive false discovery rate: a Bayesian interpretation and the q-value

This work introduces a modified version of the FDR called the “positive false discovery rate” (pFDR), which can be written as a Bayesian posterior probability and can be connected to classification theory.

On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics

A new approach to problems of multiple significance testing was presented in Benjamini and Hochberg (1995), which calls for controlling the expected ratio of the number of erroneous rejections to the

Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach

Summary.  The false discovery rate (FDR) is a multiple hypothesis testing quantity that describes the expected proportion of false positive results among all rejected null hypotheses. Benjamini and

A direct approach to false discovery rates

The calculation of the q‐value is discussed, the pFDR analogue of the p‐value, which eliminates the need to set the error rate beforehand as is traditionally done, and can yield an increase of over eight times in power compared with the Benjamini–Hochberg FDR method.

Controlling the false discovery rate: a practical and powerful approach to multiple testing

SUMMARY The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to

Multiple hypotheses testing and expected number of type I. errors

The performance of multiple test procedures with respect to error control is an old issue. Assuming that all hypotheses are true we investigate the behavior of the expected number of type I errors

Adaptive linear step-up procedures that control the false discovery rate

The linear step-up multiple testing procedure controls the false discovery rate at the desired level q for independent and positively dependent test statistics. When all null hypotheses are true, and

Improving false discovery rate estimation

A method called the spacings LOESS histogram (SPLOSH) is proposed for estimating the conditional FDR, the expected proportion of false positives conditioned on having k 'significant' findings, and is designed to be more stable than the q-value and applicable in a wider variety of settings than BUM.

Augmentation Procedures for Control of the Generalized Family-Wise Error Rate and Tail Probabilities for the Proportion of False Positives

Any single-step or stepwise multiple testing procedure controlling the family-wise error rate (FWER) can be augmented into procedures that (asymptotically) control tail probabilities for the number of false positives and the proportion offalse positives among the rejected hypotheses.

Multiple Testing. Part I. Single-Step Procedures for Control of General Type I Error Rates

The present article proposes general single-step multiple testing procedures for controlling Type I error rates defined as arbitrary parameters of the distribution of the number of Type I errors,