• Corpus ID: 209988898

A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology.

@article{Hejazi2017AGO,
  title={A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology.},
  author={Nima S. Hejazi and Philippe Boileau and Mark J. van der Laan and Alan E. Hubbard},
  journal={arXiv: Methodology},
  year={2017}
}
The widespread availability of high-dimensional biological sequencing data has made the simultaneous screening of numerous biological characteristics a central statistical problem in computational biology. While the dimensionality of such data sets continues to increase, the problem of teasing out the effects of biomarkers in studies measuring baseline confounders while avoiding model misspecification remains only partially addressed. Efficient estimators constructed from data adaptive… 

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References

SHOWING 1-10 OF 47 REFERENCES
Targeted Learning: Causal Inference for Observational and Experimental Data
TLDR
This work focuses on TMLE in Adaptive Group Sequential Covariate Adjusted RCTs, which involves cross-Validated Targeted Minimum-Loss-Based Estimation and targeted Bayesian Learning.
Causality: Models, Reasoning and Inference
1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5.
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
  • G. Smyth
  • Mathematics
    Statistical applications in genetics and molecular biology
  • 2004
TLDR
The hierarchical model of Lonnstedt and Speed (2002) is developed into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples and the moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom.
limma: Linear Models for Microarray Data
TLDR
This chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments with technical as well as biological replication.
Biomarker discovery using targeted maximum‐likelihood estimation: Application to the treatment of antiretroviral‐resistant HIV infection
TLDR
A new approach to research questions of this type, based on targeted maximum‐likelihood estimation of variable importance measures is introduced, which aims to learn which of a set of candidate biomarkers is important in determining a given outcome.
biotmle: Targeted Learning for Biomarker Discovery
TLDR
The biotmle package provides an implementation of a biomarker discovery methodology based on targeted minimum loss-Based estimation (TMLE) and a generalization of the moderated t-statistic of (Smyth 2004), designed for use with biological sequencing data.
Assessing exposure effects on gene expression
TLDR
The regression, IPW, and g-formula approaches to exposure effect estimation are compared herein using simulations; advantages and disadvantages of each approach are explored.
Big Data, Small Sample: Edgeworth Expansions Provide a Cautionary Tale
Multiple comparisons and small sample size, common characteristics of many types of “Big Data” including those that are produced by genomic studies, present specific challenges that affect
A Simple Sequentially Rejective Multiple Test Procedure
This paper presents a simple and widely ap- plicable multiple test procedure of the sequentially rejective type, i.e. hypotheses are rejected one at a tine until no further rejections can be done. It
Oracle inequalities for multi-fold cross validation
TLDR
The results are extended to penalized cross validation in order to control unbounded loss functions and applications include regression with squared and absolute deviation loss and classification under Tsybakov’s condition.
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