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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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## Improving PSF calibration in confocal microscopic imaging -- estimating and exploiting bilateral symmetryNicolai Bissantz, Hajo Holzmann, and Miroslaw PawlakVolume 4 Issue 4, pg. 1871-1891

#### Supplements

 Title Estimating bilateral symmetry: Technical details Description Here we provide the technical proofs for our results in the paper "Improving PSF calibration in confocal microscopic imaging---estimating and exploiting bilateral symmetry." DOI 10.1214/10-AOAS343SUPP Link http://lib.stat.cmu.edu/aoas/343/supplement.pdf

## Testing Affiliation in Private-Values Models of First-Price Auctions using Grid DistributionsHarry J. Paarsch and Luciano I. de CastroVolume 4 Issue 4, pg. 2073-2098

#### Supplements

 Title Monte Carlo Study Description In this supplement, we discribe a small-scale Monte Carlo study used to investigate the numerical as well as small-sample properties of our testing strategy. DOI 10.1214/00-AOAS344SUPP Link http://lib.stat.cmu.edu/aoas/344/supplement.pdf

## Multicategory Vertex Discriminant Analysis for High-Dimensional DataTong Tong Wu and Kenneth LangeVolume 4 Issue 4, pg. 1698-1721

#### Supplements

 Title Proof of Proposition 1 Description We prove Fisher consistency of $\varepsilon$-insensitive loss in this paper. DOI 10.1214/10-AOAS345SUPP Link http://lib.stat.cmu.edu/aoas/345/supplement.pdf

## An Imputation Based Approach for Parameter Estimation in Reliability with Ambiguous CensoringSamiran GhoshVolume 4 Issue 4, pg. 1976-1999

#### Supplements

 Title Furnace Data Set and R Code for Furnace Data as well as Simulation for all Models Considered in the Paper Description R~code is used for the simulation as well as real data analysis. Supplementary material has five files: 1. Furnace data in MS Excel format 2. Code for analyzing furnace data 3. Code for the Exponential--Exponential model 4. Code for the Exponential--Weibull model 5. Code for the Weibull--Exponential model For the simulation examples data sets are generated on the fly at the beginning of the code. No special R package is required to run the codes. All the codes are commented for the ease of understanding. DOI 10.1214/10-AOAS348SUPP Link http://lib.stat.cmu.edu/aoas/348/supplement.zip

## Reuse, Recycle, Reweigh: Combating Influenza through Efficient Sequential Bayesian Computation for Massive DataJennifer A. Tom, Janet S. Sinsheimer, and Marc A. SuchardVolume 4 Issue 4, pg. 1722-1748

#### Supplements

 Title Details of sampling from the complete model Description We detail the sampling steps for our complete model outlined in Section Complete and our constrained covariance matrices model outlined in Section Constrained. DOI 10.1214/10-AOAS349SUPP Link http://lib.stat.cmu.edu/aoas/349/supplement.pdf

## A bivariate-space time downscaler under space and time misalignmentVeronica J. Berrocal, Alan E. Gelfand, and David M. HollandVolume 4 Issue 4, pg. 1942-1975

#### Supplements

 Title Fitting details Description This section provides details for fitting the bivariate downscaler model. In the section we will first illustrate how to fit the general bivariate downscaler model in its static version, and then we will discuss how to adapt the fitting model procedures from the static setting to the space-time setting. DOI 10.1214/10-AOAS351SUPP Link http://lib.stat.cmu.edu/aoas/351

## Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel MethodTingting Zhang and Samuel KouVolume 4 Issue 4, pg. 1913-1941

#### Supplements

 Title Technical proofs Description Technical proofs accompanying the paper "Nonparametric inference of doubly stochastic Poisson process data via the kernel method" by Zhang and Kou. DOI 10.1214/10-AOAS352SUPP Link http://lib.stat.cmu.edu/aoas/352/supplement.pdf

## Exit Polling and Racial Bloc Voting: Combining Individual-Level and R x C Ecological DataDaniel James Greiner and Kevin M. QuinnVolume 4 Issue 4, pg. 1774-1796

#### Supplements

 Title Supplement to "Exit polling and racial bloc voting: Combining individual-level and R x C ecological data" Description This supplement describes the algorithms used to fit the models described in "Exit polling and racial bloc voting: Combining individual-level and R x C ecological data." DOI 10.1214/10-AOAS353SUPPA Link http://lib.stat.cmu.edu/aoas/353/SupplementA.gz

## Generalized Extreme Value Regression for Binary Response Data: An Application to B2B Electronic Payments System AdoptionXia Wang and Dipak K. DeyVolume 4 Issue 4, pg. 2000-2023

#### Supplements

 Title R codes for GEV models with covariates Description The computation for the GEV link described in this paper has been implemented in R which is available in this supplementary material. DOI 10.1214/10-AOAS354SUPP Link http://lib.stat.cmu.edu/aoas/354/supplement.txt

## Zero-Inflated Truncated Generalized Pareto Distribution for the Analysis of Radio Audience DataDominique Laurent Couturier and Maria-Pia Victoria-FeserVolume 4 Issue 4, pg. 1824-1846

#### Supplements

 Title Radio data set and R Code Description The file "data\_ZITPo.csv" contains the data set analyzed in Section Application. The observations are in rows and the variables in columns. The file "functions\_ZITPo.r" contains R functions that allow to fit and analyze ZITPo models. It produces objects of class "zipto." Usual generic functions are then available for objects of that class. The file "script\_ZITPo.r" contains the R Code used to produce the results of Tables zitpo116 and lrt116 and the plots of Figure zitpo116B. DOI 10.1214/10-AOAS358SUPP Link http://lib.stat.cmu.edu/aoas/358/supplement.zip

## Model-Robust Regression and a Bayesian 'Sandwich' EstimatorAdam Szpiro, Ken Rice, and Thomas LumleyVolume 4 Issue 4, pg. 2099-2113

#### Supplements

 Title Proofs of theorems in "Model robust regression and a Bayesian 'sandwich' estimator' (Szpiro, Rice, and Lumley) Description We provide proofs of the theorems stated in the paper "Model robust regression and a Bayesian 'sandwich' estimator' by Adam A. Szpiro, Kenneth M. Rice and Thomas Lumley. DOI 10.1214/10-AOAS362SUPP Link http://lib.stat.cmu.edu/aoas/362/supplement.pdf

## Subsampling Methods for Genomic InferencePeter J Bickel, Nathan Boley, James B Brown, Haiyan Huang, and Nancy R ZhangVolume 4 Issue 4, pg. 1660-1697

#### Supplements

 Title Some theorems in subsampling methods for genomic inference Description In Supplementary Material, we provide theoretical proofs to the theorems presented in the main text. DOI 10.1214/10-AOAS363SUPP Link http://lib.stat.cmu.edu/aoas/363/supplement.pdf

## Modelling heterogeneity in ranked data: a non-parametric likelihood approachBrian Francis, Regina Dittrich, and Reinhold HatzingerVolume 4 Issue 4, pg. 2181-2202

#### Supplements

 Title The EM algorithm for NPML random effects in ranked data Description We provide a detailed description of the use of the EM algorithm for fitting nonparametric random effects for ranked data by maximum likelihood. DOI 10.1214/10-AOAS366SUPP Link http://lib.stat.cmu.edu/aoas/366/supplement.pdf

## Bayesian Semsiparametric Inference for Multivariate Doubly-Interval-Censored DataAlejandro Jara, Emmanuel Lesaffre, Maria De Iorio, and Fernando QuintanaVolume 4 Issue 4, pg. 2126-2149

#### Supplements

 Title MCMC schemes for posterior computation Description A complete description of the full conditionals for marginal and conditional MCMC algorithms for fitting the LDPD survival model for doubly-interval-censored data is given. DOI 10.1214/10-AOAS368SUPPA Link http://lib.stat.cmu.edu/aoas/368

 Title The HIV-AIDS data Description The analysis of the data set considered by (degruttolalagakos89) is presented. This analysis allows for the comparison of the LDPD model with the one-sample nonparametric maximum likelihood estimator proposed by (degruttolalagakos89). The data set considers information from a cohort of hemophiliacs at risk of human immunodeficiency virus (HIV) infection from infusions of blood they received periodically to treat their hemophilia in two hospitals in France. For this cohort both infection with HIV and the onset of acquired immunodeficiency syndrome (AIDS) or other clinical symptoms could be subject to censoring. Therefore, the induction time between infection and clinical AIDS are treated as doubly-censored. DOI 10.1214/10-AOAS368SUPPB Link http://lib.stat.cmu.edu/aoas/368

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