A Journal Devoted To All Areas Of Applied Statistics

Annals of Applied Statistics
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 Volume 1, Number 1 (2007) Volume 1, Number 2 (2007) Volume 2, Number 1 (2008) Volume 2, Number 2 (2008) Volume 2, Number 3 (2008) Volume 2, Number 4 (2008) Volume 3, Number 1 (2009) Volume 3, Number 2 (2009) Volume 3, Number 3 (2009) Volume 3, Number 4 (2009) Volume 4, Number 1 (2010) Volume 4, Number 2 (2010) Volume 4, Number 3 (2010) Volume 4, Number 4 (2010) Volume 5, Number 1 (2011) Volume 5, Number 2a (2011) Volume 5, Number 2b (2011) Volume 5, Number 3 (2011) Volume 5, Number 4 (2011) Future Issues
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## Statistical analysis of an archeological findAndrey FeuervergerVolume 2 Issue 1, pg. 3-54

#### Supplements

 Title Computing code for "Statistical analysis of an archeological find'' Description This file contains the R computing code used to produce the results in this paper. The code is self-explanatory and is easily modified to generate the reported results. It may also be modified to account for different assumption sets to enter into the "RR" (relevance and rareness) computations. DOI 10.1214/08-AOAS99supp Link http://lib.stat.cmu.edu/aoas/99/supplement.txt

## Discussion of: Statistical analysis of an archaeological findRandall IngermansonVolume 2 Issue 1, pg. 84-90

#### Supplements

 Title Analysis of the Talpiot tomb using Bayes' Theorem and random variables Description We analyze the Talpiot tomb, which has been alleged to be the family tomb of Jesus of Nazareth. Using Bayes' Theorem, we derive a simple function that estimates the probability that the tomb houses the remains of Jesus and his family. Unfortunately, this function cannot be evaluated exactly, because several of the key parameters are unknown. By using random variables with reasonable probability distributions, we examine the mean behavior and range of the function under a variety of conditions. We conclude that the probability is low (on the order of 2% or less) that the Talpiot tomb is the family tomb of Jesus of Nazareth. DOI 10.1214/08-AOAS99GSUPP Link http://lib.stat.cmu.edu/aoas/99g/supplement.pdf

## Zero-Inflated Poisson Regression Models for Randomized response Sum Score DataMaarten Jan Leo Frans Cruyff, Ulf Bockenholt, Ardo van den Hout, and Peter G.M. van der HeijdenVolume 2 Issue 1, pg. 316-331

#### Supplements

 Title The social security survey data Description The survey was conducted in 2004 by the Dutch Department of Social Affaires amongst 870 social security beneficiaries. The data contain the responses to five randomized response items assessing noncompliance with social security regulations, and seven background variables. DOI 10.1214/07-AOAS135SUPP Link http://lib.stat.cmu.edu/aoas/135/supplement.zip

## In-season prediction of batting averages: a field test of empirical Bayes and Bayes methodologiesLawrence D. BrownVolume 2 Issue 1, pg. 136-139

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 Title Major league batting records for 2005 Description The file gives monthly batting records (AB and H) for each Major League baseball players for the 2005 season. The names of the players are given, as well as a designation as to whether the player is a pitcher or not a pitcher. DOI 10.1214/07-AOAS138supp Link http://lib.stat.cmu.edu/aoas/138/supplement.zip

## Transcription Factor Binding Site Prediction with Multivariate Gene Expression DataNancy Ruonan Zhang, Mary Wildermuth, and Terence SpeedVolume 2 Issue 1, pg. 332-365

#### Supplements

 Title Additional tables and figures Description Supplementary Figures 1 and 2 show respectively the principal components and screeplot for Spellman et al. (1998) yeast cell cycle data set. Supplementary Table 1 (a-b) shows gene list enrichment, annotation, and flanking sequence analysis for promoter elements identified in Spellman et al. (1998) yeast cell cycle experiment. Supplementary Table 2 (a-b) shows the same information for the Wildermuth et al. (2007) arabidopsis powerdery mildew infection experiment. Supplementary table 3 lists the genes containing the (CGCGTT, TTTCCA, 200) element. DOI 10.1214/07-AOAS142SUPP Link http://lib.stat.cmu.edu/aoas/142/supplements.zip

## Optimal Factorial Designs for cDNA Microarray ExperimentsTathagata Bandyopadhyay, and Rahul MukerjeeVolume 2 Issue 1, pg. 366-385

#### Supplements

 Title Optimal factorial designs for CDNA microarray experiments: Proofs Description Technical details, including proofs, appear in a supplementary material file posted at the journal website. DOI 10.1214/07-AOAS144SUPP Link http://lib.stat.cmu.edu/aoas/144/supplement.pdf

## A Hidden Spatial-temporal Markov Random Field Model for Network-based Analysis of Time Course Gene Expression DataZhi Wei and Hongzhe LiVolume 2 Issue 1, pg. 408-429

#### Supplements

 Title Details on simulations and comparison with the HMM model Description We present detailed simulation results in Tables 1S-S2, including the standard errors of the sensitivities, specificities and FDRs. We also present the time course expression profiles (Figures S1-S2) of the genes that were identified by our methods but missed by the HMM method and the genes that were identified by the HMM method but missed by our methods. DOI 10.1214/07-AOAS145SUPP Link http://lib.stat.cmu.edu/aoas/145/supplement.pdf

## A Statistical Framework for Testing Functional Categories in Microarray DataWilliam T. Barry, Andrew B. Nobel, and Fred A. WrightVolume 2 Issue 1, pg. 286-315

#### Supplements

 Title Measures of differential expression in gene category testing Description In gene category testing, global statistics typically fall into two groups: "categorical'' statistics that rely on a list of significant genes to be identified, and "continuous'' statistics that incorporate real-valued measures of gene-specific differential expression. The following figure illustrates the two data types using the notation framework described in the article. DOI 10.1214/07-AOAS146SUPPA Link http://lib.stat.cmu.edu/aoas/146/supplement-figure.pdf

 Title Variance of the Wilcoxon rank sum statistic under correlation Description The variance of the Wilcoxon rank sum global statistic in equation (4.5) is derived in the following theorem under the assumption of dependent and identically distributed Gaussian local statistics. The proof is presented using the notation framework described in the article, and is analogous to the classic work of Gastwirth and Rubin. DOI 10.1214/07-AOAS146SUPPB Link http://lib.stat.cmu.edu/aoas/146/supplement-theorem.pdf

## Coordinate Descent Algorithms for Lasso Penalized RegressionTong Tong Wu and Kenneth LangeVolume 2 Issue 1, pg. 224

#### Supplements

 Title Weak Consistency of Lasso Penalized L1 Regression Description Our supplementary appendix demonstrates the weak consistency of penalized L1 estimators. The proof is a straightforward adaptation of the arguments of Oberhofer (1983) on nonlinear L1 regression. Since we only consider linear models, the regularity conditions in Oberhofer (1983) are relaxed and clarified. DOI 10.1214/07-AOAS147SUPP Link http://lib.stat.cmu.edu/aoas/147/supplement.pdf

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