develops a procedure for optimally assigning college
Log-Linear Models for Gene Association roommates, exploiting estimated interaction effects
Jianhua Hu, Adarsh Joshi, and Valen E. Johnson to boost academic performance. And Catriona
Mapping Ancient Forests: Bayesian Inference for Spatio-Temporal
Queen and Casper Albers study intervention and
Trends in Forest Composition Using the Fossil Pollen Proxy Record
causality in traffic flows.
Christopher J. Paciorek and Jason S. McLachlan This does not begin to exhaust the range of appli-
Regression Models for Identifying Noise Sources in Magnetic
cations in this issue, nor does this short catalog do
Resonance Images
justice to the authors. It is an exciting collection of
Hongtu Zhu, Yimei Li, Joseph G. Ibrahim, Xiaoyan Shi,
papers, and showcases the broad impact of our field.
Hongyu An, Yashen Chen, Wei Gao, Weili Lin, Daniel B. Rowe,
and Bradley S. Peterson Theory and Methods
Intrinsically Autoregressive Spatio-Temporal Models with
The Theory and Methods Section leads with the
Application to Aggregated Birth Outcomes discussion paper “On Consistency and Sparsity
Jonathan D. Norton and Xu-Feng Niu
for Principal Components Analysis in High
Sequential Design for Microarray Experiments
Dimensions,” by Iain M. Johnstone and Arthur
Gilles Durrieu and Laurent Briollais Yu Lu. The authors argue that for principal compo-
Doubly Robust Internal Benchmarking and False Discovery Rates
nents analysis with large-p-small-n data “some ini-
for Detecting Racial Bias in Police Stops
tial reduction in dimensionality is desirable before
Greg Ridgeway and John M. MacDonald
applying any PCA-type search for principal modes,
[and that] the initial reduction in dimensionality is
Intervention and Causality: Forecasting Traffic Flows Using a
Dynamic Bayesian Network
best achieved by working in a basis in which the sig-
Catriona M. Queen and Casper J. Albers
nals have a sparse representation.” Discussion papers
are by B. Nadler; D. Whitten, T. Hastie, and R.
Theory and Methods
Tibshirani; and J. Ramsay.
On Consistency and Sparsity for Principal Components Analysis in
Functional data are addressed in “Estimating
High Dimensions
Derivatives for Samples of Sparsely Observed
Iain M. Johnstone and Arthur Yu Lu Functions, with Application to Online Auction
Estimating Derivatives for Samples of Sparsely Observed Functions,
Dynamics” by Bitao Liu and Hans-Georg Müller
with Application to Online Auction Dynamics
and “On the Concept of Depth for Functional
Bitao Liu and Hans-Georg Müller
Data” by Sara López-Pintado and Juan Romo,
whereas shrinkage in high-dimensional problems
On the Concept of Depth for Functional Data
Sara López-Pintado and Juan Romo
is the topic of “Partial Correlation Estimation
by Joint Sparse Regression Models” by Jie
Partial Correlation Estimation by Joint Sparse Regression Models
Peng, Pei Wang, Nengfeng Zhou, and Ji Zhu
Jie Peng, Pei Wang, Nengfeng Zhou, and Ji Zhu
and “Shrinkage Estimation of the Varying
Shrinkage Estimation of the Varying Coefficient Model
Coefficient Model” by Hansheng Wang and
Hansheng Wang and Yingcun Xia
Yingcun Xia.
Bayesian Mixture Labeling by Highest Posterior Density
Bayesian contributions include “Bayesian
Weixin Yao and Bruce G. Lindsay Mixture Labeling by Highest Posterior Density”
Prior Distributions from Pseudo-Likelihoods in the Presence of
by Weixin Yao and Bruce G. Lindsay and “Prior
Nuisance Parameters
Distributions from Pseudo-Likelihoods in the
Laura Ventura, Stefano Cabras, and Walter Racugno Presence of Nuisance Parameters” by Laura Ventura,
Confidence Intervals for Population Ranks in the Presence of Ties
Stefano Cabras, and Walter Racugno.
and Near Ties
Theory and Methods is rounded out with inter-
Minge Xie, Kesar Singh, and Cun-Hui Zhang
esting contributions on confidence intervals in
“Confidence Intervals for Population Ranks in the
Computationally Efficient Nonparametric Importance Sampling
Jan C. Neddermeyer
Presence of Ties and Near Ties” by Minge Xie, Kesar
Singh, and Cun-Hui Zhang; importance sampling
A Class of Transformed Mean Residual Life Models with Censored
in “Computationally Efficient Nonparametric
Survival Data
Importance Sampling” by Jan C. Neddermeyer;
Liuquan Sun and Zhigang Zhang
survival analysis in “A Class of Transformed Mean
A Multivariate Extension of the Dynamic Logit Model for
Residual Life Models with Censored Survival Data”
Longitudinal Data Based on a Latent Markov Heterogeneity
by Liuquan Sun and Zhigang Zhang; binary lon-
Structure
Francesco Bartolucci and Alessio Farcomeni
gitudinal data in “A Multivariate Extension of the
Dynamic Logit Model for Longitudinal Data Based
Hunting for Significance with the False Discovery
on a Latent Markov Heterogeneity Structure” by
Martin Posch, Sonja Zehetmayer, and Peter Bauer
Francesco Bartolucci and Alessio Farcomeni; and
JUNE 2009 AmstAt News 27
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