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Annals of Applied Statistics |
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Volume 1, Number 1 (2007) |
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Volume 1, Number 2 (2007) |
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Volume 2, Number 1 (2008) |
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Volume 2, Number 2 (2008) |
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Volume 2, Number 3 (2008) |
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Volume 2, Number 4 (2008) |
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Volume 3, Number 1 (2009) |
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Volume 3, Number 2 (2009) |
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Volume 3, Number 3 (2009) |
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Volume 3, Number 4 (2009) |
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Volume 4, Number 1 (2010) |
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Volume 4, Number 2 (2010) |
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Volume 4, Number 3 (2010) |
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Volume 4, Number 4 (2010) |
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Volume 5, Number 1 (2011) |
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Volume 5, Number 2a (2011) |
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Volume 5, Number 2b (2011) |
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Statistical analysis of an archeological find
Andrey Feuerverger
Volume 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 find
Randall Ingermanson
Volume 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
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Zero-Inflated Poisson Regression Models for Randomized response Sum Score Data
Maarten Jan Leo Frans Cruyff, Ulf Bockenholt, Ardo van den Hout, and Peter G.M. van der Heijden
Volume 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 methodologies
Lawrence D. Brown
Volume 2 Issue 1, pg. 136-139
Supplements
| 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 Data
Nancy Ruonan Zhang, Mary Wildermuth, and Terence Speed
Volume 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 Experiments
Tathagata Bandyopadhyay, and Rahul Mukerjee
Volume 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 Data
Zhi Wei and Hongzhe Li
Volume 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 Data
William T. Barry, Andrew B. Nobel, and Fred A. Wright
Volume 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 Regression
Tong Tong Wu and Kenneth Lange
Volume 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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