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Annals of Applied Statistics |
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Editorial Board |
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Published Issues |
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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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Volume 5, Number 3 (2011) |
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Volume 5, Number 4 (2011) |
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Future Issues |
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Instructions for Referees |
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Letters to Editor |
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Bayesian variable selection using cost-adjusted BIC, with application to cost-effective measurement of quality of health care
Dimitris Fouskakis, Ioannis Ntzoufras, and David Draper
Volume 3 Issue 2, pg. 663-690
Supplements
| Title |
Cost-based prior distributions for variable selection in generalized linear models |
| Description |
Imaginary data and power-prior motivation for the prior distribution in the
main paper's equation (6) and details on RJMCMC and MC^3
implementation and utility elicitation. |
| DOI |
10.1214/08-AOAS207SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/207/supplement.pdf |
A general likelihood framework for the Zelterman-estimator of the population size
Peter van der Heijden and Dankmar Boehning
Volume 3 Issue 2, pg. 595-610
Supplements
Network exploration via the adaptive LASSO and SCAD penalties
Jianqing Fan, Yang Feng, and Yichao Wu
Volume 3 Issue 2, pg. 521-541
Supplements
Experimental Designs for Educational Studies with Responses at Multiple Levels
Brenda Jenney and Sharon Lohr
Volume 3 Issue 2, pg. 691-709
Supplements
| Title |
Proof of information for student model when $\bD_i$ is balanced
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| Description |
Proofs of equations eq:tr_d_with and eq:tr_d_without appear in a supplementary file posted at the
journal website. |
| DOI |
10.1214/08-AOAS216SUPPA |
| Link |
http://lib.stat.cmu.edu/aoas/216/supplementA.pdf |
| Title |
SAS program for simulation of anticipated variance
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| Description |
A SAS macro lets the user input simulation scenarios,
including variance components, number of schools, number of teachers
and students at each school, and contamination coefficient, then
generates comparison graphs of the density of the anticipated variance
for the three randomization designs under the teacher and student
models, along with empirical power estimates. |
| DOI |
10.1214/08-AOAS216SUPPB |
| Link |
http://lib.stat.cmu.edu/aoas/216/supplementB.zip |
Estimating Limits From Poisson Counting Data Using Dempster-Shafer Analysis
Paul Thatcher Edlefsen, Chuanhai Liu, and Arthur Pentland Dempster
Volume 3 Issue 2, pg. 764-790
Supplements
| Title |
A complete derivation of the DS solution to the Banff challenge |
| Description |
We provide a complete derivation of the Dempster--Shafer
solution, including detail and special cases not covered in the \hyperref[app]{Appendix}. |
| DOI |
10.1214/08-AOAS223SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/223/supplement.pdf |
A Hierarchical Dirichlet Process Mixture Model for Haplotype Reconstruction from Multi-Population Data
Eric Xing, and Kyung-Ah Sohn
Volume 3 Issue 2, pg. 791-821
Supplements
| Title |
More results on sensitivity analysis and description of real data |
| Description |
We provide the sensitivity analysis result to the
hyper-parameters of HDP on diverse dataset theta=0.05), and the
details of the real data from 10 HapMap ENCODE regions used in Figure
f_real.
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| DOI |
10.1214/08-AOAS225SUPPB |
| Link |
http://lib.stat.cmu.edu/aoas/225/supplement.pdf |
An Estimating Equations Approach to Fitting Laten Exposure Models with Longitudinal Responses
Brisa N Sanchez, Esben Budtz-Joergensen, and Louise M Ryan
Volume 3 Issue 2, pg. 830-856
Supplements
| Title |
An estimating equations approach to fitting
latent exposure
models with longitudinal responses
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| Description |
We provide details on the simulation parameters
referenced in Sections 5.1, 5.2 and 5.3. We
also provide details on variance and relative efficiency calculations
mentioned in Sections 4.2 and 5.2, respectively. |
| DOI |
10.1214/08-AOAS226SUPPA |
| Link |
http://lib.stat.cmu.edu/aoas/226/supplement.pdf |
| Title |
An estimating equations approach to fitting
latent exposure
models with longitudinal responses
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| Description |
This zipped folder contains several files with example
code for the procedures described in this article.
For specific details on the files, read the "readme.txt'" file found
within this zipped folder. |
| DOI |
10.1214/08-AOAS226SUPPB |
| Link |
http://lib.stat.cmu.edu/aoas/226/SupplementBCode.zip |
Bayesball: A Bayesian Hierarchical Model for Evaluating Fielding in Major League Baseball
Shane T. Jensen, Kenny Shirley, and Abraham J. Wyner
Volume 3 Issue 2, pg. 491-520
Supplements
| Title |
Gibbs sampling implementation |
| Description |
We provide details of our Markov chain Monte Carlo
implementation, which is based on the Gibbs sampling GemGem84and the data augmentation approach of AlbChi93. |
| DOI |
10.1214/08-AOAS228SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/228/supplement.pdf |
Latent Markov model for binary longitudinal data: an application to the performance evaluation of nursing homes
Francesco Bartolucci, Monia Lupparelli, and Giorgio Edoardo Montanari
Volume 3 Issue 1, pg. 611-636
Supplements
| Title |
Matlab functions and dataset used in the application developed within
the paper "Latent Markov model for binary longitudinal data: an
application to the performance evaluation of nursing homes" |
| Description |
The approach described in this paper has been
implemented in a series of Matlab functions which are available
from Bartolucci, Lupparelli and Montanari
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| DOI |
10.1214/08-AOAS230SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/230/supplement.zip |
Prediction of Remaining Life of Power Transformers Based on Left Truncated and Right Censored Lifetime Data
Yili Hong, William Q. Meeker, and James D. McCalley
Volume 3 Issue 2, pg. 857-879
Supplements
| Title |
Supplement to "Prediction of remaining life of power
transformers based on
left truncated and right censored lifetime data''
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| Description |
This supplement provides a description of the
difficulties that we
had in fitting a model to describe the failure behavior of the MD
group data. The problems arise because of the large amount of
truncation in this particular group. |
| DOI |
10.1214/08-AOAS231SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/231/supplement.pdf |
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