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Volume 5, Number 1 (2011) |
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On Bayesian "Central Clustering": Application to Landscape Classification of Western Ghats
Sabyasachi Mukhopadhyay, Sourabh Bhattacharya, and Kajal Dihidar
Volume 5 Issue 3, pg. 1948-1977
Supplements
| Title |
Supplement to On Bayesian "central clustering":
Application to landscape classification of Western Ghats
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| Description |
Sections S-1 and S-2 contain, respectively, the full
conditional distributions of the
random variables with respect
to the nonmarginalized and marginalized version of SB's model. That the
K-means clustering
algorithm is a special case
of the clustering method based on SB's model is shown in Section S-3.
Properties of the approximate distance
measure d are explored in Section S-4. Section S-5 contains
reports of our investigation on whether
or not the spatial structure of the superpixels should be incorporated
in our model. Detailed analysis of
sensitivity of the results with respect to changes in the values of the
hyperparameters of our model
is provided in Section S-6. Thorough explanation of the computational
superiority of SB's model
over that associated with efficient implementation of EW's model is
presented in Section S-7.
Finally, a new method for MCMC convergence diagnostics in clustering
models is proposed
in Section S-8, which we apply in our situation for studying convergence
of our Markov chain.}
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| DOI |
10.1214/11-AOAS454SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/454/supplement.pdf |
Are Private Schools Really Better Than Public Schools? Assessment by Methods for Observational Studies
Danny Pfeffermann and Victoria Landsman
Volume 5 Issue 3, pg. 1726-1751
Supplements
| Title |
Supplement to: "Are private schools better than public schools? Appraisal for Ireland by methods for observational studies"
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| Description |
This supplement contains a PDF which is
divided into five sections:
[Supplement A] develops the probability weighted estimators of the ATE.
[Supplement B] describes the maximization of the likelihood
[Supplement C] contains the proof of Lemma
[Supplement D] contains the proof of Result
[Supplement E] describes the data file, which is provided
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| DOI |
10.1214/11-AOAS456SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/456/supplement.zip |
Semiparametric modeling of autonomous nonlinear dynamical systems with application to plant growth
Debashis Paul, Jie Peng, and Prabir Burman
Volume 5 Issue 3, pg. 2078-2108
Supplements
| Title |
Supplement to "Semiparametric modeling of autonomous
nonlinear dynamical systems with application to plant growth"
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| Description |
The supplementary materials provide additional details
on the computational schemes. It also contains further simulation
studies elucidating the performance of the proposed estimators under
scenarios not covered in the main article.
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| DOI |
10.1214/11-AOAS459SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/459/supplement.pdf
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A method for visual identification of small sample subgroups and potential biomarkers
Charlotte Soneson and Magnus Fontes
Volume 5 Issue 3, pg. 2131-2149
Supplements
| Title |
Supplementary material
|
| Description |
In the supplementary material we give a small schematic example showing the
different steps of CUMBIA. Further, we show how to emphasize both over- and
underexpressed variables in the visualization and how the choice of K and s
affect the resulting visualization. We also provide scree plots obtained by
CUMBIA and PCA for the three data sets studied in the paper.
|
| DOI |
10.1214/11-AOAS460SUPPA |
| Link |
http://lib.stat.cmu.edu/aoas/460/supplementA.pdf |
| Title |
Supplementary figures---Projection pursuit results
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| Description |
The supplementary figures show the result of the
FastICA projection pursuit algorithm applied to the three data
sets considered in the paper. Note that to facilitate the interpretation
of the figures, the axes are ungraded and only the origin is marked.
|
| DOI |
10.1214/11-AOAS460SUPPB |
| Link |
http://lib.stat.cmu.edu/aoas/460/supplementB.pdf |
Incorporating Biological Information in Bayesian Models for the Selection of Pathways and Genes
Yian A. Chen, Francesco C. Stingo, Mahlet G. Tadesse, and Marina Vannucci
Volume 5 Issue 3, pg. 1978-2002
Supplements
Spatial Modelling of Extreme Snow Depth
Juliette Blanchet and Anthony Davison
Volume 5 Issue 3, pg. 1699-1725
Supplements
| Title |
Supplementary Material for "Spatial modeling of extreme snow depth"
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| Description |
This contains example time series of data, and further
discussion of the estimation algorithm and of the fitted models.
|
| DOI |
10.1214/11-AOAS464SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/464/supplement.pdf |
Generalized genetic association study with samples of related individuals
Zeny Z Feng, William W Wong, Xin Gao, and Flavio Schenkel
Volume 5 Issue 3, pg. 2109-2130
Supplements
| Title |
Mathematical justifications and additional results
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| Description |
The supplementary materials of the paper are organized
as follows.
Appendix~A provides the theoretical justification of the
variance--covariance matrix Sig_0.
Appendix~B derives the explicit form of the W_G statistic for a
biallelic marker in a single
pedigree study design. Appendix~C derives the expression of the W_G
statistic for a multi-allelic
marker in a single pedigree study design. In Appendix~D additional
results of simulation studies and
the results of COGA data analysis are summarized in tables.
|
| DOI |
10.1214/11-AOAS465SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/465/supplement.pdf |
Block-based Bayesian Epistasis Association Mapping with Application to WTCCC Type 1 Diabetes Data
Yu Zhang, Jing Zhang, and Jun S. Liu
Volume 5 Issue 3, pg. 2052-2077
Supplements
| Title |
Additional Supporting Information and Results
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| Description |
The
file includes a naive SNP-block model used in our comparison,
verification of population structure in the sample, LD analysis of the
MHC region, Chi-square results of our simulation study, and comparison
of our results with previous results in the T1D WTCCC1 data.
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| DOI |
10.1214/11-AOAS469SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/469/supplement.doc |
A Bayesian Joinpoint Regression model with an unknown number of break points
Miguel Angel Martínez Beneito, Gonzalo García-Donato, and Diego Salmeron
Volume 5 Issue 3, pg. 2150-2168
Supplements
| Title |
Supplement Document
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| Description |
A supplemental document for this paper has been written
containing further details about: Performance of the proposed methods
on simulated data sets, Calculus of the basis functions allowing the
fitted trends to describe joinpoints and some remarks about Bayes
factors and their computation in our specific setting.
This document can be found at Martinez-BeneitoGarcia-Donatoea2011b
|
| DOI |
10.1214/11-AOAS471SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/471/supplement.pdf |
Local Kernel Canonical Correlation Analysis with Application to Virtual Screening
Daniel Victor Samarov, J.S. Marron, Yufeng Liu, Christopher Grulke, and Alexander Tropsha
Volume 5 Issue 3, pg. 2169-2196
Supplements
| Title |
Local kernel canonical correlation analysis with application
to virtual
drug screening
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| Description |
Drug discovery is the process of identifying compounds
which have potentially
meaningful biological activity. A major challenge that arises is that the
number of compounds to search over can be quite large, sometimes
numbering in
the millions, making experimental testing intractable. For this reason
computational methods are employed to filter out those compounds which
do not
exhibit strong biological activity. This filtering step, also called virtual
screening reduces the search space, allowing for the remaining
compounds to be
experimentally tested.
In this paper we propose several novel approaches to the problem of virtual
screening based on Canonical Correlation Analysis (CCA) and on a kernel based
extension. Spectral learning ideas motivate our proposed new method called
Indefinite Kernel CCA (IKCCA). We show the strong performance of this approach
both for a toy problem as well as using real world data with dramatic
improvements in predictive accuracy of virtual screening over an existing
methodology.}
|
| DOI |
10.1214/11-AOAS472SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/472/supplement.pdf |
A Space-Time Varying Coefficient Model: The Equity ofService Accessibility
Nicoleta Serban
Volume 5 Issue 3, pg. 2024-2051
Supplements
| Title |
Supplemental Material
|
| Description |
The supplemental materials accompanying this paper are divided into
seven sections:
{Supplement 1.} Varying-coefficient model---Decomposition of the
design matrix under the tensor-product decomposition of the space--time
varying coefficients.
{Supplement 2.} Varying-coefficient model---Derivation of the
confidence bands for the space and time varying coefficients.
{Supplement 3.} Varying-coefficient model---A simulation study
under multiple predictors.
{Supplement 4.} Varying-coefficient model---Proof of Proposition.
{Supplement 5.} Case study---Description of ESRI data.
{Supplement 6.} Case study---Accessibility maps for Atlanta
area.
{Supplement 7.} Case study---Results and maps for the
provider-level accessibility analysis.}
|
| DOI |
10.1214/11-AOAS473SUPP |
| Link |
http://lib.stat.cmu.edu/aoas/473/supplement.pdf |
Estimating Within-Household Contact Networks from Egocentric Data
Gail Elizabeth Potter, Mark S. Handcock, Ira M. Longini, Jr., and M. Elizabeth Halloran
Volume 5 Issue 3, pg. 1816-1838
Supplements
| Title |
Contact network parameters estimated separately for the
holiday period versus the nonholiday period, and for 2--3 member
households versus 4+ member households
|
| Description |
We present parameter estimates computed separately for
respondents who reported during the Easter holiday period and during a
nonholiday period. Next we report parameters estimated separately for
households with 2--3 members and those with 4+ members.
|
| DOI |
10.1214/11-AOAS474SUPPA |
| Link |
http://lib.stat.cmu.edu/aoas/474/Supplement\%20A.pdf |
| Title |
Results from simulation study exploring weak identifiability
|
| Description |
We present simulation results evaluating weak
identifiability of our parameters in data sets with low
within-household contact rates and low at-home probabilities. |
| DOI |
10.1214/11-AOAS474SUPPB |
| Link |
http://lib.stat.cmu.edu/aoas/474/Supplement\%20B.pdf |
The Potential for Bias in Principal Causal Effect Estimation When Treatment Received Depends on a Key Covariate
Corwin M. Zigler and Thomas R. Belin
Volume 5 Issue 3, pg. 1876-1892
Supplements
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