Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.
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Replication data for: Diverse Correlation Structures in Microarray Gene Expression Data
Replication data for: Diverse Correlation Structures in Microarray Gene Expression Data
Jan 3, 2008
Lev Klebanov; Andrei Yakovlev, 2008, "Replication data for: Diverse Correlation Structures in Microarray Gene Expression Data", https://doi.org/10.7910/DVN/Z6UE8D, Harvard Dataverse, V1
It is well-known that correlations in microarray data represent a serious nuisance deteriorating the performance of gene selection procedures. This paper is intended to demonstrate that the correlation structure of microarray data provides a rich source of useful information. We discuss distinct correlation substructures revealed in microarray gene...
AOAS0703-010R2A0.pdf
AOAS0703-010R2A0.pdf
Jan 3, 2008 - Replication data for: Diverse Correlation Structures in Microarray Gene Expression Data
Adobe PDF - 203.6 KB - MD5: fabe599d443b6a6edb3fe665debe452f
1. Documentation
Original article for this study
supplement1.pdf
supplement1.pdf
Jan 3, 2008 - Replication data for: Diverse Correlation Structures in Microarray Gene Expression Data
Adobe PDF - 30.7 KB - MD5: 57599d66860ac981f4db346ddcb41e0b
1. Documentation
Effects of cross-hybridization and their relevance to correlation analysis of microarray data
supplement2.pdf
supplement2.pdf
Jan 3, 2008 - Replication data for: Diverse Correlation Structures in Microarray Gene Expression Data
Adobe PDF - 256.5 KB - MD5: 98f17f48fd75aa1f68c3b3ffc5018cb4
1. Documentation
The figures presented are counterparts of Figure 2 in the main text; they refer to other sets of microarray data.
supplement3.pdf
supplement3.pdf
Jan 3, 2008 - Replication data for: Diverse Correlation Structures in Microarray Gene Expression Data
Adobe PDF - 16.7 KB - MD5: 822e7d17d0e284ce9306e2a4312807a4
1. Documentation
Supporting material for Remark 3
supplement4.pdf
supplement4.pdf
Jan 3, 2008 - Replication data for: Diverse Correlation Structures in Microarray Gene Expression Data
Adobe PDF - 17.4 KB - MD5: d88a054590cc014725eed7cc0c20f82b
1. Documentation
Distribution of the effect sizes specified for 350 elements of the sequence
supplement5.pdf
supplement5.pdf
Jan 3, 2008 - Replication data for: Diverse Correlation Structures in Microarray Gene Expression Data
Adobe PDF - 1.1 MB - MD5: 1b302fe1e0eeb135af36d1cc8095ee30
1. Documentation
Outcomes of testing in the course of subsampling
Replication data for: Coupling of Hidden Markov Models for the Discovery of Cis-Regulatory Modules in Multiple Species
Replication data for: Coupling of Hidden Markov Models for the Discovery of Cis-Regulatory Modules in Multiple Species
Nov 27, 2007
Qing Zhou; Wing Hung Wong, 2007, "Replication data for: Coupling of Hidden Markov Models for the Discovery of Cis-Regulatory Modules in Multiple Species", https://doi.org/10.7910/DVN/P1FC4F, Harvard Dataverse, V1
Cis-regulatory modules (CRMs) composed of multiple transcription factor binding sites (TFBSs) control gene expression in eukaryotic genomes. Comparative genomic studies have shown that these regulatory elements are more conserved across species due to evolutionary constraints. We propose a statistical method to combine module structure and cross-sp...
supplement.pdf
supplement.pdf
Nov 27, 2007 - Replication data for: Coupling of Hidden Markov Models for the Discovery of Cis-Regulatory Modules in Multiple Species
Adobe PDF - 57.7 KB - MD5: bfeee3fde144d3568b9243e818cb7821
1. Documentation
Coupling Hidden Markov Models for the Discovery of Cis-Regulatory Modules in Multiple Species (Supplemental Notes)
Replication data for: A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields
Replication data for: A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields
Nov 27, 2007
Brian James Reich; Montserrat Fuentes, 2007, "Replication data for: A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields", https://doi.org/10.7910/DVN/PMF6PG, Harvard Dataverse, V1
Storm surge, the onshore rush of sea water caused by the high winds and low pressure associated with a hurricane, can compound the effects of inland flooding caused by rainfall, leading to loss of property and loss of life for residents of coastal areas. Numerical ocean models are essential for creating storm surge forecasts for coastal areas. Thes...
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