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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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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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Improving PSF calibration in confocal microscopic imaging -- estimating and exploiting bilateral symmetry
Nicolai Bissantz, Hajo Holzmann, and Miroslaw Pawlak
Volume 4 Issue 4, pg. 1871-1891
Supplements
| Title |
Estimating bilateral symmetry: Technical details
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| 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."
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| 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 Distributions
Harry J. Paarsch and Luciano I. de Castro
Volume 4 Issue 4, pg. 2073-2098
Supplements
| Title |
Monte Carlo Study
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| 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.
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| DOI |
10.1214/00-AOAS344SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/344/supplement.pdf
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Multicategory Vertex Discriminant Analysis for High-Dimensional Data
Tong Tong Wu and Kenneth Lange
Volume 4 Issue 4, pg. 1698-1721
Supplements
An Imputation Based Approach for Parameter Estimation in Reliability with Ambiguous Censoring
Samiran Ghosh
Volume 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
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| 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.
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| 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 Data
Jennifer A. Tom, Janet S. Sinsheimer, and Marc A. Suchard
Volume 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.
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| DOI |
10.1214/10-AOAS349SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/349/supplement.pdf |
A bivariate-space time downscaler under space and time misalignment
Veronica J. Berrocal, Alan E. Gelfand, and David M. Holland
Volume 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.
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| DOI |
10.1214/10-AOAS351SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/351 |
Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method
Tingting Zhang and Samuel Kou
Volume 4 Issue 4, pg. 1913-1941
Supplements
| Title |
Technical proofs
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| Description |
Technical proofs accompanying the paper "Nonparametric inference of doubly
stochastic Poisson process data via the kernel method" by Zhang and Kou.
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| 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 Data
Daniel James Greiner and Kevin M. Quinn
Volume 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"
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| 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."
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| 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 Adoption
Xia Wang and Dipak K. Dey
Volume 4 Issue 4, pg. 2000-2023
Supplements
| Title |
R codes for GEV models with covariates
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| Description |
The computation for the GEV link described in this
paper has been implemented in R which is available in this
supplementary material.
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| 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 Data
Dominique Laurent Couturier and Maria-Pia Victoria-Feser
Volume 4 Issue 4, pg. 1824-1846
Supplements
| Title |
Radio data set and R Code
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| 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.
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| DOI |
10.1214/10-AOAS358SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/358/supplement.zip |
Model-Robust Regression and a Bayesian 'Sandwich' Estimator
Adam Szpiro, Ken Rice, and Thomas Lumley
Volume 4 Issue 4, pg. 2099-2113
Supplements
| Title |
Proofs of theorems in "Model robust regression and a Bayesian
'sandwich' estimator' (Szpiro, Rice, and Lumley)
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| 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.
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| DOI |
10.1214/10-AOAS362SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/362/supplement.pdf |
Subsampling Methods for Genomic Inference
Peter J Bickel, Nathan Boley, James B Brown, Haiyan Huang, and Nancy R Zhang
Volume 4 Issue 4, pg. 1660-1697
Supplements
| Title |
Some theorems in subsampling methods for genomic inference
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| Description |
In Supplementary Material, we provide theoretical proofs to the theorems presented in the main text.
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| DOI |
10.1214/10-AOAS363SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/363/supplement.pdf |
Modelling heterogeneity in ranked data: a non-parametric likelihood approach
Brian Francis, Regina Dittrich, and Reinhold Hatzinger
Volume 4 Issue 4, pg. 2181-2202
Supplements
| Title |
The EM algorithm for NPML random effects in ranked data
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| Description |
We provide a detailed description of
the use of the EM algorithm for fitting nonparametric random
effects for ranked data by maximum likelihood.
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| DOI |
10.1214/10-AOAS366SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/366/supplement.pdf |
Bayesian Semsiparametric Inference for Multivariate Doubly-Interval-Censored Data
Alejandro Jara, Emmanuel Lesaffre, Maria De Iorio, and Fernando Quintana
Volume 4 Issue 4, pg. 2126-2149
Supplements
| Title |
MCMC schemes for posterior computation
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| 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.
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| 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.
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| DOI |
10.1214/10-AOAS368SUPPB |
| Link |
http://lib.stat.cmu.edu/aoas/368 |
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