|
 |
|
|
|
Annals of Applied Statistics |
| |
Submissions |
| |
Supplement Instructions |
| |
Subscriptions |
| |
Editorial Board |
|
Next Issues |
| |
Published Issues |
 |
Supplements |
| |
 |
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 |
|
| |
Instructions for Referees |
| |
Letters to Editor |
|
|
|
|
| |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
A New Latent Cure Rate Marker Model for Survival Data
Ming-Hui Chen, Sungduk Kim, and Yingmei Xi
Volume 3 Issue 3, pg. 1124-1146
Supplements
| Title |
Finding Large Average Submatrices in High Dimensional Data |
| Description |
In online supplementary material we provide the empirical results for
checking the proportional hazards, assumption and the description of the Markov
chain Monte Carlo (MCMC) sampling algorithm for a fixed $G$ and
the detailed development of the reversible jump MCMC .
|
| DOI |
10.1214/08-AOAS238SUPP} |
| Link |
http://lib.stat.cmu.edu/aoas/238/supplement.pdf |
Finding Large Average Submatrices in High Dimensional Data
Andrey A Shabalin, Victor J Weigman, Charles M Perou, and Andrew B Nobel
Volume 3 Issue 3, pg. 985-1012
Supplements
| Title |
Supplement to Finding Large Average Submatrices in High-Dimensional Data |
| Description |
The supplementary article contains additional tables and figures used
in validation of LAS.
It includes bar plots similar to Figure 2 produced for
each cancer subtype,
a complete set of validation results for the lung cancer gene
expression data set from bhattacharjee et al (2001),
and tables with simulation results measuring stability and noise
sensitivity of the LAS model and algorithm.
|
| DOI |
10.1214/09-AOAS239SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/239/supplement.pdf
|
Analysis of Minnesota Colon and Rectum Cancer Point Patterns with Spatial and Non-spatial Covariate Information
Shengde Liang, Bradley P. Carlin, and Alan E. Gelfand
Volume 3 Issue 3, pg. 943-962
Supplements
| Title |
Computational Issues |
| Description |
We provide full details of the Monte Carlo algorithms
needed to approximate the complex point process likelihoods in the
paper. In particular, we flesh out the details of our knot-based
predictive process approximation, and give general guidelines for how
the knots should be selected in any given application. |
| DOI |
10.1214/09-AOAS240SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/240/supplement.pdf |
Bayesian Testing of Many Hypothesis X Many Genes: A Study of Sleep Apnea
Shane T. Jensen, Ibrahim Erkan, Erna S. Arnardottir, and Dylan S. Small
Volume 3 Issue 3, pg. 1080-1101
Supplements
| Title |
Gibbs sampling implementation and full list of hypotheses
|
| Description |
We provide details of our Markov chain Monte Carlo
implementation, which is based on a Gibbs sampling algorithm (Geman and Geman,
1984). We also give a full enumeration of the hypotheses
considered in Section 3. |
| DOI |
10.1214/09-AOAS241SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/241/supplement.pdf |
Statistical Modeling of the Time Course of Tantrum Anger
Peihua Qiu, Rong Yang, and Michael Potegal
Volume 3 Issue 3, pg. 1013-1034
Supplements
| Title |
Tantrum data and R Code |
| Description |
This is the tantrum anger data analyzed in the paper. The data
has 10 columns. The first 8 columns denote the binary status of the 8 angry
behaviors, with 1 denoting "present'' and 0 "absent.'' The 9th column is
the duration of a tantrum episode, and the 10th column is the standardized
observation time. The data are ordered by duration (i.e., the 9th column).
This is a R code fitting the final model selected by QIC
presented in Figures 3 and 4 and Table 3 of the paper. |
| DOI |
10.1214/09-AOAS242SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/242/supplement.zip |
GaGa: a Parsimonious and Flexible Model for High-Throughput Data Analysis
David Rossell
Volume 3 Issue 3, pg. 1035-1051
Supplements
| Title |
Supplement to GaGa: A parsimonious and flexible model for differential expression analysis |
| Description |
We detail an EM algorithm and two fully Bayesian MCMC schemes for model fitting,
and a Bayesian procedure for FDR control. We also assess model goodness-of-fit,
assess the quality of the gamma approximation to the gamma shape distribution
and detail the gene ontology analysis performed for the MAQC study.
|
| DOI |
10.1214/09-AOAS244SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/244/supplement.pdf |
Efficient simulation from finite-state, continuous-time Markov chains with incomplete observations
Asger Hobolth and Eric Alan Stone
Volume 3 Issue 3, pg. 1204-1231
Supplements
| Title |
Efficient simulation from finite-state, continuous-time Markov chains
with incomplete observations
|
| Description |
We accompany our paper with R code that can
reproduce the figures in the manuscript HoSt09. A
description of how the code is organized is included in the
supplementary material. |
| DOI |
10.1214/09-AOAS247SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/247/supplement.zip |
Doubly-Stochastic Continuous-Time Hidden Markov Approach for Analyzing Genome Tiling Arrays
William Evan Johnson, Xiaole Shirley Liu, and Jun S. Liu
Volume 3 Issue 3, pg. 1183-1203
Supplements
| Title |
Likelihood, ECM/MCMC algorithms, and additional results and comparisons |
| Description |
Here we provide a detailed likelihood equation and a
description of the ECM and MCMC algorithms used in this paper. In
particular, we provide details on the forward--backward and
forward--backward sampling algorithms used to infer the hidden Markov chain.
|
| DOI |
10.1214/09-AOAS248SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/248/supplement.pdf |
Hierarchical spatial models for predicting tree species assemblages across large domains
Andrew Oliver Finley, Sudipto Banerjee, and Ronald McRoberts
Volume 3 Issue 3, pg. 1052-1079
Supplements
| Title |
Description of MCMC sampling algorithm and supplementary results |
| Description |
Here we provide a description of the Metropolis scheme used to fit
the candidate models. Parameter estimates for the FTGs are also presented. |
| DOI |
10.1214/09-AOAS250SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/250/supplement.pdf |
|
|
|
|
|
|
|
