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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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Instructions for Referees |
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High Frequency Market Microstructure Noise Estimates and Liquidity Measures
Yacine Ait-Sahalia, and Jialin Yu
Volume 3 Issue 1, pg. 422-457
Supplements
| Title |
High frequency market microstructure noise estimates and liquidity measures |
| Description |
We use simulations to examine the properties of high frequency
market microstructure noise and volatility estimators. We then estimate the
noise and volatility from high frequency transaction data of NYSE stocks in the
sample period of 1995--2005. The supplemental file includes computer code in Matlab
used in the simulations and the estimation, the noise and volatility estimates, and
other data in the paper. The supplemental file also details the vendors of the
copyrighted data used in the paper.
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| DOI |
10.1214/08-AOAS200SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/200/supplement.zip |
Time series analysis via mechanistic models
Carles Breto, Daihai He, Edward L Ionides, and Aaron A King
Volume 3 Issue 1, pg. 319-348
Supplements
Practical large-scale spatio-temporal modeling of particulate matter concentrations
Christopher Joseph Paciorek, Jeff D. Yanosky, Robin C. Puett, Francine Laden, and Helen H. Suh
Volume 3 Issue 1, pg. 370-397
Supplements
| Title |
Supplementary discussion of alternative models and measurement
error implications
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| Description |
We first consider several alternative statistical
specifications for the spatial and regression terms in the model,
including kriging, concluding that none of the alternatives improve
upon the predictive performance of our core model. Next we consider the
measurement error implications of using the model predictions in an
epidemiological analysis as a covariate, arguing that the exposure
modeling takes the form of regression calibration with the implication
of limited bias in health analyses. However,
the assessment does leave aside sources of error we cannot quantify
that may reflect classical measurement error
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| DOI |
10.1214/08-AOAS204SUPPA |
| Link |
http://lib.stat.cmu.edu/aoas/204/supplement.pdf |
Multilevel Functional Principal Component Analysis
Chongzhi Di, Ciprian M Crainiceanu, Brian S Caffo, and Naresh M Punjabi
Volume 3 Issue 1, pg. 458-488
Supplements
| Title |
Multilevel functional principal component
analysis
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| Description |
We assess the criterion for choosing the number of
principal components, provide details for Bayesian MCMC for
estimating principal component scores, and show additional results
for simulations and the application to SHHS. We also provide some
technical details for the variance and covariance of the residuals
from the projection model. |
| DOI |
10.1214/08-AOAS206SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/206/supplement.pdf |
Modeling Substitution and Indel Processes for AFLP Marker Evolution and Phylogenetic Inference
Ruiyan Luo, and Bret Larget
Volume 3 Issue 1, pg. 222-248
Supplements
| Title |
AFLP data for sedges |
| Description |
The data contains 126 markers from 2 plates for 14
species. The first column denotes the marker length. The names of these species are abbreviated as:
Be (Carex bebbii), Bi (bicknellii), F (C.
festucacea), N (C. normalis), O (C. oronensis),
Te (C. tenera var. echinodes), Tt (C. tenera var.
tenera) and Ti (C. tincta). |
| DOI |
10.1214/08-AOAS212SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/212/supplement.pdf |
Measuring multivariate predictive ability of financial market movements: A latent factor framework for ordinal data
Philippe Huber, Olivier Scaillet, and Maria-Pia Victoria-Feser
Volume 3 Issue 1, pg. 249-271
Supplements
| Title |
Datasets on the predictions by two broker--dealers and
realized values on several markets
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| Description |
In this supplement, we provide a zip file containing two Excel files
for the predictions and the realized market values
of the two broker--dealers analyzed in this paper.
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| DOI |
10.1214/08-AOAS213SUPPA |
| Link |
http://lib.stat.cmu.edu/aoas/213/supplement-A.zip |
| Title |
C code for data analysis and simulations |
| Description |
In this supplement we provide a zip file containing the source code in
C for the programs used to analyze the datasets and to perform the
simulation study in this paper.
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| DOI |
10.1214/08-AOAS213SUPPB |
| Link |
http://lib.stat.cmu.edu/aoas/213/supplement-B.zip |
| Title |
Technical developments and proofs |
| Description |
In this supplement we provide the technical developments for the
likelihood comparison between the polychoric correlation and the
GLLVM of Section relationships, the development of the LAMLE for
ordered multinomial distributed
manifest variables as a complement of Section estimation and the
proofs of Propositions prop_Canon-Correl -- Pro_as-normality.
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| DOI |
10.1214/08-AOAS213SUPPC |
| Link |
http://lib.stat.cmu.edu/aoas/213/supplement.pdf |
Statistical Analysis of Stellar Evolution
David A van Dyk, Steven DeGennaro, Nathan Stein, William H Jefferys, and Ted von Hipple
Volume 3 Issue 1, pg. 117-143
Supplements
| Title |
Statistical analysis of stellar evolution: online supplement |
| Description |
This supplement contains four color figures and a description of the
physics behind the computer-based stellar evolution models. This material was
originally intended to be included in this article, but was removed for editorial
reasons. The images are visually impressive but not central to our statistical
analysis. The section on the computer model provides details for readers interested
in the inner workings of the likelihood function used in this article. |
| DOI |
10.1214/08-AOAS219SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/219/supplement.pdf |
Inference for the Dark Energy Equation of State Using Type IA Supernova Data
Christopher R Genovese, Peter Freeman, Larry Wasserman, Robert C Nichol, and Christopher Miller
Volume 3 Issue 1, pg. 144-178
Supplements
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